{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymongo\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = pymongo.MongoClient('192.168.10.48',17001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc= db['db_parker']['jsl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_list = []\n",
    "for item in doc.find({},{'html':0,'resp':0,'content':0}):\n",
    "    del item['_id']\n",
    "    total_list.append(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(total_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crawlTime</th>\n",
       "      <th>createTime</th>\n",
       "      <th>creator</th>\n",
       "      <th>last_resp_date</th>\n",
       "      <th>question_id</th>\n",
       "      <th>resp_no</th>\n",
       "      <th>title</th>\n",
       "      <th>update_time</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>2019-12-06 13:49</td>\n",
       "      <td>缓慢投资</td>\n",
       "      <td>2019-12-16 12:41:00</td>\n",
       "      <td>340297</td>\n",
       "      <td>10</td>\n",
       "      <td>为赚66元的期权的小小套利</td>\n",
       "      <td>2019-12-18 20:57:19.897</td>\n",
       "      <td>https://www.jisilu.cn/question/340297&amp;sort_key...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>2019-12-06 14:15</td>\n",
       "      <td>HENINHAO</td>\n",
       "      <td>2019-12-07 09:41:00</td>\n",
       "      <td>340299</td>\n",
       "      <td>0</td>\n",
       "      <td>谈妥了?降进口罐睡</td>\n",
       "      <td>2019-12-18 20:57:16.024</td>\n",
       "      <td>https://www.jisilu.cn/question/340299&amp;sort_key...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>2019-12-06 14:34</td>\n",
       "      <td>88924835</td>\n",
       "      <td>2019-12-06 23:18:00</td>\n",
       "      <td>340302</td>\n",
       "      <td>2</td>\n",
       "      <td>不懂就问：如何开三个封闭基金帐号？</td>\n",
       "      <td>2019-12-18 20:57:16.198</td>\n",
       "      <td>https://www.jisilu.cn/question/340302&amp;sort_key...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>2019-12-06 14:48</td>\n",
       "      <td>blsbls</td>\n",
       "      <td>2019-12-08 23:52:00</td>\n",
       "      <td>340303</td>\n",
       "      <td>8</td>\n",
       "      <td>WSJ: 中方表示，贸易谈判仍在正常轨道上。</td>\n",
       "      <td>2019-12-18 20:57:16.602</td>\n",
       "      <td>https://www.jisilu.cn/question/340303&amp;sort_key...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>2019-12-06 14:48</td>\n",
       "      <td>甜橙飘飘</td>\n",
       "      <td>2019-12-06 21:52:00</td>\n",
       "      <td>340304</td>\n",
       "      <td>1</td>\n",
       "      <td>今天上市的 132021 中国软件 可交换债 19中电EB，感觉是个坑货！</td>\n",
       "      <td>2019-12-18 20:57:16.393</td>\n",
       "      <td>https://www.jisilu.cn/question/340304&amp;sort_key...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             crawlTime        createTime   creator      last_resp_date  \\\n",
       "0  2019-12-06 17:18:15  2019-12-06 13:49      缓慢投资 2019-12-16 12:41:00   \n",
       "1  2019-12-06 17:18:15  2019-12-06 14:15  HENINHAO 2019-12-07 09:41:00   \n",
       "2  2019-12-06 17:18:15  2019-12-06 14:34  88924835 2019-12-06 23:18:00   \n",
       "3  2019-12-06 17:18:15  2019-12-06 14:48    blsbls 2019-12-08 23:52:00   \n",
       "4  2019-12-06 17:18:15  2019-12-06 14:48      甜橙飘飘 2019-12-06 21:52:00   \n",
       "\n",
       "  question_id  resp_no                                  title  \\\n",
       "0      340297       10                          为赚66元的期权的小小套利   \n",
       "1      340299        0                              谈妥了?降进口罐睡   \n",
       "2      340302        2                      不懂就问：如何开三个封闭基金帐号？   \n",
       "3      340303        8                 WSJ: 中方表示，贸易谈判仍在正常轨道上。   \n",
       "4      340304        1  今天上市的 132021 中国软件 可交换债 19中电EB，感觉是个坑货！   \n",
       "\n",
       "              update_time                                                url  \n",
       "0 2019-12-18 20:57:19.897  https://www.jisilu.cn/question/340297&sort_key...  \n",
       "1 2019-12-18 20:57:16.024  https://www.jisilu.cn/question/340299&sort_key...  \n",
       "2 2019-12-18 20:57:16.198  https://www.jisilu.cn/question/340302&sort_key...  \n",
       "3 2019-12-18 20:57:16.602  https://www.jisilu.cn/question/340303&sort_key...  \n",
       "4 2019-12-18 20:57:16.393  https://www.jisilu.cn/question/340304&sort_key...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 147074 entries, 0 to 147073\n",
      "Data columns (total 9 columns):\n",
      "crawlTime         147074 non-null object\n",
      "createTime        147074 non-null object\n",
      "creator           146664 non-null object\n",
      "last_resp_date    147074 non-null datetime64[ns]\n",
      "question_id       147074 non-null object\n",
      "resp_no           147074 non-null int64\n",
      "title             147074 non-null object\n",
      "update_time       147074 non-null datetime64[ns]\n",
      "url               147074 non-null object\n",
      "dtypes: datetime64[ns](2), int64(1), object(6)\n",
      "memory usage: 10.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "147074"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(total_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'creator': 'greatbear',\n",
       "  'title': '各位研究技术指标有什么心得体会？',\n",
       "  'content': '各位研究技术指标多久了？研究这东西，能帮炒股赚钱么：）',\n",
       "  'resp_no': 11,\n",
       "  'createTime': '2018-12-14 08:38',\n",
       "  'crawlTime': '2019-03-16 09:54:43',\n",
       "  'url': 'https://www.jisilu.cn/question/297952',\n",
       "  'resp': [{'seeker24680_0': ['0', '可以说很多不明觉厉的词，适合用于忽悠别人，具体效果同风水先生。']},\n",
       "   {'joyfulli_1': ['0',\n",
       "     '首先要知道技术指标是怎么来的\\n对过去的数据进行统计分析，然后进行预测\\n说白了就是统计学\\n所以，学过统计学就知道技术分析怎么用了']},\n",
       "   {'老高0813_2': ['0', '基本没卵用']},\n",
       "   {'z383788052_3': ['0', '从来只看指标，只看k']},\n",
       "   {'花园小琴_4': ['0', '看图形，一般只看低买高卖，但事后后悔，赚少了']},\n",
       "   {'风险搬运工_5': ['0', '趋势（多和空）和震荡结合得做（同时做），不做单边、所谓轮动策略。\\n倾向于胜率低，赔率高的策略。']},\n",
       "   {'smag_6': ['0', '基本不看指标，静下心感受波动，在波动中下注。。。']},\n",
       "   {'海浪头头_7': ['0', '同意美棠子的看法']},\n",
       "   {'jsl0900_8': ['0', '从技术指标上看，我可以预测所有交易标的的后续走势，至于准确不准确，那不是我关心的问题']},\n",
       "   {'美棠子_9': ['4', '只是个辅助工具，基本面是核心，切不可颠倒主次。']},\n",
       "   {'蔓越橘_10': ['0', '用于T一下可以，其他就算了吧。']}],\n",
       "  'last_resp_date': '2018-12-14 10:14'},\n",
       " {'creator': 'yyttcc705',\n",
       "  'title': '雅砻江水电再探讨',\n",
       "  'content': '集思录关注雅砻江的人、喜欢国投川投的人很多（包括我自己哈！），有关帖子也不少，我最近看了一些资料，整理出来，供大家参考。莫据此操作！责任自负！\\n先上结论：2019年中报出来以前利空大于利多（我打算在这个时点前逐步建基本仓，待这个时点恐慌盘出来了加仓），其后逐步反转。\\n利空一：增值税12%以上返还2017.12到期（每年应退的，大概在次年收到，雅砻江公司于收到日列帐。2018年大概还应该收17年应退的6.2亿），这个利空大约19年中报即可看明白，影响所得税前利润约6.2亿（雅砻江公司2016年度收返税9.4亿，2017前三季度收6.2亿？，15年度收14.8亿，14年度收9.2亿。政策规定：2013.1到15.12止超8%即退，其后到2017.12超12%即退）。\\n利空二：2018.12所得税减半优惠基本陆续到期（减半政策：2017.12官地到期，18.12锦屏一、二级到期，桐子林很小忽略：2020.12到期），减半结束需要按15%，影响约12-7.2=4.8亿（16年度雅砻江公司列所得税7.2亿，15年度列3.1亿）。\\n以上二个利空，其影响力持续到2019年中报可以看到，我是基于雅砻江2016年报数据(营收163.9亿，税前利润80.5亿-所得税7.2亿=税后利润73.3亿)，估计得来，两个利空调整下，2016年度雅砻江净利润为63.3亿，整整少了十亿。\\n\\n利空三：大西部开发所得税率15%在2020年底到期。到时下游还要多交税约7亿元。\\n利空四：这个不确定性较大，南水北调西线，可能调走雅砻江水量40-50亿方，这个影响，估计减少雅砻江下游发电10%左右，减少中游15%左右，非常受伤！好在目前项目阻力极大，可能15年内搞不成，时间太远，则对当下影响降低；还有，如果搞成了，北方受益极大，雅砻江等等水电站可能得到部分补偿，比如转移掉实际损失的30%-50%，这个可能性不小。另外，网上盛传可能要改为从雅鲁藏布江调水，那就更加没事了。\\n\\n利多一：两河口调节效应。\\n先看葛洲坝，其设计利用140亿度/271.5万kw=5156小时，实际已经达到7000小时左右，超设计利用小时近4成。\\n雅砻江下游，总装机727万kw，2016年发电709亿度，平均利用小时才，所以，要讲潜力，肯定有的，关键是径流量要足够大，这个从哪来呢？2016年下游弃水，估计在30到60亿度。另外，同样的流量，通过调节提高水头，也可以多发电。两河口完工后，这两个方面都会有极大好处。\\n按中国国际工程咨询公司2014年3月评估会资料（这些数据通常会比较保守），两河口将增加雅砻江下游年发电量102亿度（全部视为枯期电），这个我按110度平电估计；另外将汛期电123亿度转为平枯电，这个我按增10亿度平电估计，；则雅砻江下游增发平电为120亿度，很爽吧！\\n利多二：两河口电价高。因为是多年调节，国家规定的电价高于基准价大约30%，我们按。30亿度平电估计，则中游两河口杨房沟发平电为110+78+30=218亿度。\\n以上两个利多，算法比较粗糙，综合一下，两河口杨房沟完全投产后，发平电量将增加338亿度，比2016年增加48%：很爽吧！遗憾的是，由于折旧及利息比较大，净利增加没有这么猛。假设到2023年度还能享受15%所得税率，大概可以增加利润50%以内(已考虑年分红控制在25亿左右，每年可以直接用于项目或者还贷款70亿左右)。\\n\\n网络上，流传一些关于雅砻江的错误看法：\\n1.折旧提完了，公司盈利上升，是利好：错！这个只对短炒有好处，实质是利空，因为失去了所得税抵免效应，造成实质现金流出！\\n2.公司以后项目完了，逐步把银行贷款还完，然后利润越来越高，利好：错，失去了杠杠，股东真实收益率下降。银行的借款尽量增加，股东分红尽量增加，才是最好。还银行钱，不如回购股票!\\n3.两河口将巨亏：不明白动态看问题！动态看，雅砻江保持年分红25亿元的话，6年内，内生现金420亿基本满足两河口杨房沟全部投资需要（两者2016.12大概已经投入350亿），或者有新增贷款的话可以看做是暂时的，6年内能还完。不明白从现金角度看问题、年调节水电的价格优势、及其为下游带来的发电增量及价格结构大变。',\n",
       "  'resp_no': 42,\n",
       "  'createTime': '2017-12-23 18:44',\n",
       "  'crawlTime': '2019-01-27 11:28:31',\n",
       "  'url': 'https://www.jisilu.cn/question/259131',\n",
       "  'resp': [{'xsxinyi_0': ['0', '你好，问一下，雅砻江的年报哪来的？公司官网上好像没有公布呀，国投里也没有单独雅砻江的吧']},\n",
       "   {'yyttcc705_1': ['0',\n",
       "     '@hg  消纳问题，尤其雅中直流的阻力，确实超过了我的预期。不过，相信问题会逐步解决，中游投资白花钱没有可能性。']},\n",
       "   {'lostinjungle_2': ['0',\n",
       "     '雅砻江还有一个潜在利空，就是电力市场化对于水电上网电价的冲击。看看电力市场化先行试点省份云南就知道了，华能澜沧江同样也是天赋很高，但是2014-2016年上网电价不断走低，利润连续下降，利润率跌到惨不忍睹。\\n虽然雅砻江下游锦屏官地三分之二送江苏，但是也不得不考虑市场化对其他三分之一锦屏省内电量和二滩桐子林的影响']},\n",
       "   {'林奇_3': ['0', '上网电价会随着折旧计提结束而下调，这是水电股的致命弱点']},\n",
       "   {'hg_4': ['1',\n",
       "     '楼主研究认真，不过，没有看到雅砻江最大的问题\\n雅江最大的的问题是消纳，仅有下游的情况下，每年都要弃水几十亿，中上游建好，后果更是不堪设想\\n被上一代两投散户期盼的“雅中直流”原来据称2018年完工，现在呢？N年了，连方案都没影，为什么？人家江西已经饱和的不要不要的，“无法接纳外来电“。\\n楼主能找到哪怕一丁点靠谱的输出可能吗？\\n在电力普遍供过于求的情况下，雅砻江最大的问题，就是中游几百亿投资建好即弃水，全部打水漂@yyttcc705']},\n",
       "   {'迟来一步_5': ['0', '谢楼主分享']},\n",
       "   {'yyttcc705_6': ['0', '雅砻江，越来越诱人了。来吧来吧，我等着你！']},\n",
       "   {'yyttcc705_7': ['1', '欢迎长电粉！不过这说辞好肉麻。']},\n",
       "   {'英雄贵姓_8': ['0', '请继续把握长江电力的关键的唯一的大概率向上窗口继续享受高分红高增长回报，长江电力你的唯一选择！']},\n",
       "   {'yyttcc705_9': ['2', '所以，所谓价投，面对暴跌无所畏惧，不改变信仰，不去找暂时退出理由，反而乐呵呵加仓，真的很难。']},\n",
       "   {'yyttcc705_10': ['0', '楼上讲的好！']},\n",
       "   {'wsxgg_11': ['1',\n",
       "     '去年上半年两投都在护盘股票池，所以表现不错，每次指数大跌都翻红。 川投下半年被抛弃了，国投公布配股后也被抛弃了。国家队收缩战线，电力股只管中核浙能长电三大龙头，就这么简单，别想多了，并不是长电有多好。当然，国家队选择股票池也是有讲究的。']},\n",
       "   {'neo牛3_12': ['0', '比特币不值钱了。那云南水电可以炸掉了。\\n\\n长江电力独木难支。。。']},\n",
       "   {'lostinjungle_13': ['0', '雅砻江确实是基本面很优秀，奈何电力是管制行业， 利润一高，政府就看不顺眼，就要来薅羊毛，哎']},\n",
       "   {'yyttcc705_14': ['0', '今天恐慌盘明显。连川投之类都往下跳。']},\n",
       "   {'yugong1994_15': ['0', '学习！']},\n",
       "   {'wsxgg_16': ['0', '感觉水电还有很多雷，火电反而没多少潜在利空了，死猪不怕开水烫。']},\n",
       "   {'wsxgg_17': ['2',\n",
       "     '未来利空么，锦苏线参与江苏市场化竞价，落地价按江苏火电市场价而不是标杆价，这是江苏省文件里有的。留川渝电量参与四川水电市场价竞价，四川省文件也有。两河口标杆电价高没用，参与市场化竞价后都是浮云，四川可以参考桐子林和大渡河，云南可以参考小湾糯扎渡。总之就是，电价有很大的下降空间。']},\n",
       "   {'xeron2005_18': ['0', '学习']},\n",
       "   {'xeron2005_19': ['0', '学习']},\n",
       "   {'yyttcc705_20': ['0', '川投先炸一回，这么想破位，难道市场期待国投也仍颗业绩炸弹？']},\n",
       "   {'Jupiter_21': ['0', '南水北调西线那个，纯属子虚乌有。']},\n",
       "   {'yyttcc705_22': ['0', '谢楼上信息。不过还只是征求意见稿。可以期待。']},\n",
       "   {'中国梦_23': ['2',\n",
       "     '2017年8月国家能源局综合司下发了关于征求对《关于减轻可再生能源领域涉企税费负担的通知》意见的函，明确水电增值税新政策。\\n新政策明确，单个项目装机容量5万千瓦及以上的水电站销售水力发电电量，增值税税率按照13%征收（原税率为17%）。\\n超过100万千瓦的水电站（含抽水蓄能电站）销售自产电力产品，自2018年1月1日至2020年12月31日，对其增值税实际税负超过12%的部分实行即征即退政策。']},\n",
       "   {'yyttcc705_24': ['0', '葛洲坝，2016年，利用小时7235。一年301天24小时连续发电。']},\n",
       "   {'路林_25': ['1', '个人觉得这些利空已很难引起股价的大跌了，毕竟估值放在那呢，我持有川投，等待转债配售，我喜欢这个转债']},\n",
       "   {'yyttcc705_26': ['0',\n",
       "     '管理层确实有做大的倾向，而不是更多考虑股东利益。因为两投的大股东很大程度是个不可捉摸的概念。而小股东又没有足够的发言权。可是，和那些可以肆无忌惮吃人的股票比起来，两投还是相对可靠一些。比如国投的海外投资，饱受质疑，其英国风电投资1.854亿英镑16年度仅获收益0.33亿元人民币，但他印尼火电投资1.5亿美元，16年度获取收益达3.27亿人民币，一年百分之三十几，堪称暴利。所以，要客观的看待。']},\n",
       "   {'东北永吉_27': ['1', '国投川投双双再融资，这个股本越来越大，收益摊薄，管理层不咋地。他们的目的或许是最大做强，']},\n",
       "   {'yyttcc705_28': ['1',\n",
       "     '持有水电资产，有两个抗通胀逻辑，一是被压制的、缓慢的涨价，二是新水电站造价的上升。两河口最初的静态投资预算是180亿，现在已经变成400多亿，涨得比电价快多了！']},\n",
       "   {'xilinxOFDM_29': ['0',\n",
       "     '在国家大力降低制造业成本的当下，电价即使上涨，恐怕也是象征性的；火电利空太多;水电原理负荷中心，消纳是大问题']},\n",
       "   {'yyttcc705_30': ['0', '毛估下来，实际两个站造价约740亿，其中杨房沟170亿两河口570亿。']},\n",
       "   {'yyttcc705_31': ['0',\n",
       "     '关于两河口杨房沟造价。杨房沟，静态投资148.9亿+涨价预备10.4亿+利息40.7亿(占总预算的20%，占静态投资的27.3%)，总投资预算200亿。这个什么意思呢，就是假定一开工就全部借银行钱投资，利率5.46%的话，算入了5年利息！\\n这里面有两个问题，一是项目投资是逐步进去的，二是按我的思路，贷款部分基本就是已经用到的贷款，按两个站150亿瞎估计吧（2015年度雅砻江公司资本化利息5.4亿，归两个站和卡杨高路的4.5亿倒推相关借款不到100亿。假如其后又贷了几十亿元），归杨房沟约40亿，40*5.4%*5年=10.8亿，多算了30亿，为官方发布总投资的15%！这样估计两河口总投资多算利息约90亿。毛估下来，实际两个站造价约740亿，其中杨房沟170亿两河口540亿。']},\n",
       "   {'yyttcc705_32': ['0', '楼上所言有理。水电价涨幅落后于通胀。']},\n",
       "   {'Lbcylgf_33': ['23',\n",
       "     '优点很多，不提了。多说说风险。\\n从较长的时间维度看，人类历史就是一部能源价格不断下降的历史，技术进步加强了这种趋势。能源价格涨幅落后于通胀是社会常态。过去30年，有比电价涨幅更低的基本大宗商品（手机、汽车之类的工业品不算）吗？未来这种趋势难以改变。城市化与重化工化高峰期已过，加上电力属于价格管制行业，整体行业回报落在基准回报附近是大概率事件\\n电力系统多为国企，喜欢做加法乘法，不喜欢做减法。本质驱动是内部人控制的规模最大化，而不是股东利益最大化。资本运作能力差，现金流很好但往往用得一塌糊涂。\\n电力是一种标准化商品，不具备品牌效应。行业也太容易预期，天花板也很清晰，想象力略小，缺乏超额回报的空间。']},\n",
       "   {'yyttcc705_34': ['0',\n",
       "     '基准电价调整，以及雅中直流等完工导致弃水减少，以及2019两河口开始蓄水、开始起调节作用，税收优惠可能延长，等等，也将构成多空博弈点。']},\n",
       "   {'yyttcc705_35': ['0', '嗯，我没有考虑电价长期上涨。这个，各人去拍脑袋吧。']},\n",
       "   {'yyttcc705_36': ['0', '平均利用小时才4823小时。']},\n",
       "   {'yyttcc705_37': ['0', '更正一下，雅砻江下游，总装机1470万kw，设计发电727亿度。']},\n",
       "   {'fhz365_38': ['0', '楼主是认真研究了，有一定财税知识。我预计随电价上调拐点到来，2018中报数据转好。当前即是低谷。']},\n",
       "   {'fhz365_39': ['0', '楼主是认真研究了，有一定财税知识。我预计随电价上调拐点到来，2018中报数据转好。当前即是低谷。']},\n",
       "   {'hello123_40': ['0', '电力用不完，能直接挖矿嘛']},\n",
       "   {'Login255_41': ['2', '我是外行，不过看起来就舒服，赞一个。']}],\n",
       "  'last_resp_date': '2018-12-14 10:06'},\n",
       " {'creator': '共克时艰',\n",
       "  'title': '散户是弱势群体……',\n",
       "  'content': '今天才发现申万基金佣金万3，然后今天去改，死活不同意。\\n\\n主要是态度，搞的他们才是顾客，我是服务员一样~~\\n\\n所以决定换券商，初步定在华泰。\\n\\n不过开的东西比较多，又是融资融券，又是期权，路慢慢……\\n\\n哎，总结就是只有100万的散户是弱势群体……',\n",
       "  'resp_no': 11,\n",
       "  'createTime': '2018-12-17 15:21',\n",
       "  'crawlTime': '2019-03-16 09:54:19',\n",
       "  'url': 'https://www.jisilu.cn/question/298174',\n",
       "  'resp': [{'z383788052_0': ['0', '我介绍个妹子给你服务一下']},\n",
       "   {'风呼呼吹_1': ['0', '应该说小散*']},\n",
       "   {'拌拌_2': ['0',\n",
       "     '银河做债券最划算，华泰做ETF、LOF、分级A最划算。所以我二个帐户搞。\\n股票不知道哪个最划算，我10多年没有买过股票了。坚决不打新、不缴鲜花税啊...........']},\n",
       "   {'ccd001_3': ['0', '百度万一无五']},\n",
       "   {'seafreedom_4': ['0', '佣金宝还是万2.5，头次让他们调低没同意，觉得好高啊，有没有比佣金宝更好的，推荐下，谢谢']},\n",
       "   {'拌拌_5': ['2', '这种，坚决销户，立马变脸，我试过']},\n",
       "   {'赚钱买房_6': ['0', '经常有发小广告的，国泰君安，1.5/0.5/免5，可以关注']},\n",
       "   {'占位符_7': ['0', '等明年三月份的网络销户，现在AA的1.5遍地都是，还有免5的，说不定到时候更低']},\n",
       "   {'huweihuwei_8': ['2', '老客户是狗系列']},\n",
       "   {'lvshihong_9': ['0', '开了融资融券之后，东财主动挂给我要给我下调佣金到万一点五\\n只能说，楼主努力挣钱吧，有钱真好']},\n",
       "   {'kkqq999_10': ['0', '你有几百万，他们就会改的']}],\n",
       "  'last_resp_date': '2018-12-17 16:22'},\n",
       " {'creator': 'chenjie2002',\n",
       "  'title': '截止今日2.27持有的全部债券，加了杠杠0.6倍',\n",
       "  'content': '截止今日2.27持有的全部债券，加了杠杠0.6倍，加上每天会做波段，预计全部收益15%+，欢迎提意见，谢谢\\n\\n序号  代码  名称  现价  票息\\n\\n1   112326  16中弘01 Q    90.499  6.89\\n2   122417  15东旭集 Q 94.8    6.2\\n3   136203  16国创01 Q    91  6.87\\n4   124815  14天瑞02 Q    89.8    8.5\\n5   112467  16天广01 Q    82.99   5\\n7   127099  15天瑞02 Q    91.51   6.89\\n8   124112  13豫盛润 Q 92.48   7.39\\n9   122690  12三胞债 Q 92.58   8.28\\n10  136028  15花园01 Q    94.59   7.25\\n11  112334  16国购01 Q    94  7.2\\n12  136119  15国创01 Q    94.5    6.88\\n13  124989  14三星01 Q    93  9\\n14  112048  11凯迪债 Q 96.201  8.5\\n15  124038  12远洲控 Q 90.4    7.4\\n16  136093  15华信债 Q 93.86   4.98\\n17  112314  16华南01 Q    94.5    5.98\\n18  124223  13微山矿 Q 93.8    6.15\\n19  122890  10凯迪债 Q 91.48   6.12\\n20  127102  15襄矿债 Q 91.9    8.8\\n21  136584  16铁牛债 Q 97.6    6.49\\n23  136698  16申信01 Q    91.2    4.08\\n24  112240  15华联债 Q 94.489  7.5\\n25  112160  12毅昌01 Q    99.8    5.95\\n26  124187  PR泰矿债 Q 57.6    5.8\\n27  112320  16蓝标债 Q 95  3.99\\n28  136520  16永泰03 Q    97  7.5\\n29  136007  15鲁焦02 Q    97.93   6.9\\n30  122159  12亿利02 Q    92.9    6.42\\n31  122476  15天瑞债 Q 97.86   5.95\\n32  112188  13渤租债   98.36   6\\n33  122442  15鲁焦01 Q    98.7    6.9\\n34  136310  16当代01 Q    94.6    3.75\\n35  112165  12德豪债 Q 99.779  6.3\\n36  122237  12西资源 Q 99.92   7.5\\n37  122267  13永泰债 Q 99.1    7.3\\n38  112361  16新大02 Q    99.8    6.6\\n39  136210  16力帆债 Q 98  6.26\\n40  112279  15渤租01 Q    97.998  4.62\\n41  124418  13永利债 Q 99  7.5\\n42  122249  13平煤债 Q 92.9    5.07',\n",
       "  'resp_no': 35,\n",
       "  'createTime': '2018-02-27 15:18',\n",
       "  'crawlTime': '2019-01-26 04:42:01',\n",
       "  'url': 'https://www.jisilu.cn/question/265716',\n",
       "  'resp': [{'收垃圾债_0': ['0', '违约债券按5折赔付？ 想的太美']},\n",
       "   {'shshchen_1': ['0', '支持楼主。充分证明了垃圾债的系统性风险比股票的系统性风险小。']},\n",
       "   {'chenjie2002_2': ['1',\n",
       "     '现在是债券的最差时候，也是最好的时候。尽管有些债券已经违约停牌了（有的签了分期偿还协议，有的无任何结果），但是违约债券按5折赔付计算的话，目前总得居然还是盈利的。']},\n",
       "   {'FZ3600_3': ['4', '可怜的楼主，摊大饼摊在垃圾堆上。']},\n",
       "   {'cile_4': ['0', '违约了咋维权？']},\n",
       "   {'拾月_5': ['0', '只能说今年雷太多了']},\n",
       "   {'chenlei1211_6': ['0', '只能死扛了']},\n",
       "   {'mmmdoor_7': ['1',\n",
       "     '别打击楼主了，楼主估计也够苦的了。但资本市场不管这些的，坦白讲，这组合如果目前没有调整的化，不太乐观，信用风险暴露的太多了。']},\n",
       "   {'秋水a_8': ['0', '感觉全部违约了']},\n",
       "   {'债券小白_9': ['0', '楼主这组合也绝了']},\n",
       "   {'皆空_10': ['3', '有时候不能看以往的历史\\n\\n经验也害人']},\n",
       "   {'CVV227_11': ['0', '现在都是机构户，做不了维权专家了']},\n",
       "   {'魏斌杰_12': ['1', '以后就可以在雪球集思录做维权专家，一个问题888']},\n",
       "   {'zzzzv_13': ['2', '结果是在雷区逐行扫描']},\n",
       "   {'66顺_14': ['0', '现在都是机构户，维权都难。']},\n",
       "   {'akao_15': ['0', '各种花式的雷，全部维权一遍，以后可以成为维权专家了。']},\n",
       "   {'chenjie2002_16': ['0', '有部分仓位调整了，目前浮亏和死扛中。']},\n",
       "   {'1987618girl_17': ['0', '现在咋样了，分享下']},\n",
       "   {'淡定就好_18': ['3', '估计已经在维权的路上']},\n",
       "   {'五洋还钱_19': ['3', '准备经常去维权了']},\n",
       "   {'许多胸毛_20': ['0', '现在有几个点看了']},\n",
       "   {'五十年_21': ['0', '楼主现在多少杠杠，还有多少杠杠备用']},\n",
       "   {'安心就好_22': ['0', '楼主现在还有多少债券仓位']},\n",
       "   {'保守理财_23': ['1', '现在债券不敢买，上有顶下无底，太可怕了。']},\n",
       "   {'carpediem_24': ['1', '赌性十足啊']},\n",
       "   {'债券操盘手_25': ['0', '咦，居然没有136137']},\n",
       "   {'未鉴_26': ['1', '请教楼主怎么加的杠杆？']},\n",
       "   {'FZ3600_27': ['0', '仔细看了二遍，确定没有海航。']},\n",
       "   {'水与火_28': ['0', '似加息周期\\n对债市是利空\\n加杠杆做债\\n不妥']},\n",
       "   {'leo5800_29': ['0', '遇到违约就麻烦了。']},\n",
       "   {'转债痴迷者_30': ['5', '基本把垃圾债一网打尽了。']},\n",
       "   {'拒绝忽悠_31': ['0', '您给别人建议，还差不多。']},\n",
       "   {'econometrics_32': ['2',\n",
       "     '华信的债目前看随着安邦的垮掉估计市场担心不少，东旭集团的债券说不上来，但是东旭感觉还是有不少问题，账面上如此多现金为何还大举负债？']},\n",
       "   {'chenjie2002_33': ['0',\n",
       "     '重仓的有鲁焦（新大）系，华信（申信），华联，凯迪系，蓝标，铁牛，襄矿，永泰，力帆，西资源，华南']},\n",
       "   {'chenjie2002_34': ['0', '写错字了 如无违约 估计一年收益能有15%+']}],\n",
       "  'last_resp_date': '2018-12-14 10:32'},\n",
       " {'creator': 'zhjienc',\n",
       "  'title': '有人总结华泰货基赎回的坑了，我来总结一创的吧',\n",
       "  'content': '后面标X的是既能赎回且买卖佣金为0的。如有错漏请包涵。\\n\\n511600  货币ETF\\n511660  建信添益\\n511680  安信货币\\n511690  交易货币\\n511700  场内货币\\n511800  易货币\\n511810  理财金H            X\\n511820  鹏华添利            X\\n511830  华泰货币            X\\n511850  财富宝E            X\\n511860  博时货币            X\\n511880  银华日利            X\\n511890  景顺货币            X\\n511900  富国货币            X\\n511910  融通货币            X\\n511920  广发货币\\n511930  中融日盈\\n511960  嘉实快线\\n511970  国寿货币\\n511980  现金添富\\n511990  华宝添益            X',\n",
       "  'resp_no': 22,\n",
       "  'createTime': '2016-12-19 17:06',\n",
       "  'crawlTime': '2019-01-27 08:05:07',\n",
       "  'url': 'https://www.jisilu.cn/question/82064',\n",
       "  'resp': [{'FZ3600_0': ['0', '511690 511700 511850 511890 511770\\n华泰不能申赎']},\n",
       "   {'justbreath_1': ['0', '511850亲测不行']},\n",
       "   {'风险搬运工_2': ['0', '这个是挺烦的，如果自己做，需要去货币基金的公告里找。']},\n",
       "   {'饕餮海_3': ['0', '511660也是零手续费，申赎零佣金。']},\n",
       "   {'大头王_4': ['0', 'mark']},\n",
       "   {'葱花饼_5': ['0', '511890 景顺货币 X     这家伙正在准备清盘，不用考虑了！']},\n",
       "   {'rnll_6': ['0', 'mark']},\n",
       "   {'szeddy_7': ['0',\n",
       "     '511850 财富宝E X\\n511860 博时货币 X\\n511890 景顺货币 X\\n511900 富国货币 X\\n511910 融通货币 X\\n这几个可以吗？']},\n",
       "   {'葱花饼_8': ['0', '融通货币不能赎回吗？']},\n",
       "   {'05120512_9': ['0', '总结好']},\n",
       "   {'sm1314_10': ['0', '学习了 货币基金']},\n",
       "   {'atheros_11': ['0', '谢谢分享。。。']},\n",
       "   {'雪球88888_12': ['0', '511800 易货币\\n这个不能赎回么']},\n",
       "   {'sunshinemay_hy_13': ['0', '楼主说的是场外赎回吧，华安那个貌似是每天50w赎回额度，应该能赎回，不过都抢不到吧']},\n",
       "   {'hxw1052_14': ['0', '不错，收藏了！！辛苦']},\n",
       "   {'banmen_15': ['0', '赞，辛苦']},\n",
       "   {'wjq198_16': ['0', '如果有个华泰证券所有免申购赎回费用的货基表就好了']},\n",
       "   {'banmen_17': ['0', '学习']},\n",
       "   {'zdzhjx_18': ['0', '顶，顺便问问佣金宝上怎么赎回511820']},\n",
       "   {'山林野士_19': ['2', '必须顶起来']},\n",
       "   {'mingqiong_20': ['0', '511960 嘉实快线也是0手续费，我试过了']},\n",
       "   {'mingqiong_21': ['0', '好帖子，怎么没人顶呢']}],\n",
       "  'last_resp_date': '2018-12-17 16:27'}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_list[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(total_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content</th>\n",
       "      <th>crawlTime</th>\n",
       "      <th>createTime</th>\n",
       "      <th>creator</th>\n",
       "      <th>last_resp_date</th>\n",
       "      <th>resp</th>\n",
       "      <th>resp_no</th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>各位研究技术指标多久了？研究这东西，能帮炒股赚钱么：）</td>\n",
       "      <td>2019-03-16 09:54:43</td>\n",
       "      <td>2018-12-14 08:38</td>\n",
       "      <td>greatbear</td>\n",
       "      <td>2018-12-14 10:14</td>\n",
       "      <td>[{'seeker24680_0': ['0', '可以说很多不明觉厉的词，适合用于忽悠别人...</td>\n",
       "      <td>11</td>\n",
       "      <td>各位研究技术指标有什么心得体会？</td>\n",
       "      <td>https://www.jisilu.cn/question/297952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>集思录关注雅砻江的人、喜欢国投川投的人很多（包括我自己哈！），有关帖子也不少，我最近看了一些...</td>\n",
       "      <td>2019-01-27 11:28:31</td>\n",
       "      <td>2017-12-23 18:44</td>\n",
       "      <td>yyttcc705</td>\n",
       "      <td>2018-12-14 10:06</td>\n",
       "      <td>[{'xsxinyi_0': ['0', '你好，问一下，雅砻江的年报哪来的？公司官网上好像...</td>\n",
       "      <td>42</td>\n",
       "      <td>雅砻江水电再探讨</td>\n",
       "      <td>https://www.jisilu.cn/question/259131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>今天才发现申万基金佣金万3，然后今天去改，死活不同意。\\n\\n主要是态度，搞的他们才是顾客，...</td>\n",
       "      <td>2019-03-16 09:54:19</td>\n",
       "      <td>2018-12-17 15:21</td>\n",
       "      <td>共克时艰</td>\n",
       "      <td>2018-12-17 16:22</td>\n",
       "      <td>[{'z383788052_0': ['0', '我介绍个妹子给你服务一下']}, {'风呼...</td>\n",
       "      <td>11</td>\n",
       "      <td>散户是弱势群体……</td>\n",
       "      <td>https://www.jisilu.cn/question/298174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>截止今日2.27持有的全部债券，加了杠杠0.6倍，加上每天会做波段，预计全部收益15%+，欢...</td>\n",
       "      <td>2019-01-26 04:42:01</td>\n",
       "      <td>2018-02-27 15:18</td>\n",
       "      <td>chenjie2002</td>\n",
       "      <td>2018-12-14 10:32</td>\n",
       "      <td>[{'收垃圾债_0': ['0', '违约债券按5折赔付？ 想的太美']}, {'shshc...</td>\n",
       "      <td>35</td>\n",
       "      <td>截止今日2.27持有的全部债券，加了杠杠0.6倍</td>\n",
       "      <td>https://www.jisilu.cn/question/265716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>后面标X的是既能赎回且买卖佣金为0的。如有错漏请包涵。\\n\\n511600  货币ETF\\n...</td>\n",
       "      <td>2019-01-27 08:05:07</td>\n",
       "      <td>2016-12-19 17:06</td>\n",
       "      <td>zhjienc</td>\n",
       "      <td>2018-12-17 16:27</td>\n",
       "      <td>[{'FZ3600_0': ['0', '511690 511700 511850 5118...</td>\n",
       "      <td>22</td>\n",
       "      <td>有人总结华泰货基赎回的坑了，我来总结一创的吧</td>\n",
       "      <td>https://www.jisilu.cn/question/82064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             content            crawlTime  \\\n",
       "0                        各位研究技术指标多久了？研究这东西，能帮炒股赚钱么：）  2019-03-16 09:54:43   \n",
       "1  集思录关注雅砻江的人、喜欢国投川投的人很多（包括我自己哈！），有关帖子也不少，我最近看了一些...  2019-01-27 11:28:31   \n",
       "2  今天才发现申万基金佣金万3，然后今天去改，死活不同意。\\n\\n主要是态度，搞的他们才是顾客，...  2019-03-16 09:54:19   \n",
       "3  截止今日2.27持有的全部债券，加了杠杠0.6倍，加上每天会做波段，预计全部收益15%+，欢...  2019-01-26 04:42:01   \n",
       "4  后面标X的是既能赎回且买卖佣金为0的。如有错漏请包涵。\\n\\n511600  货币ETF\\n...  2019-01-27 08:05:07   \n",
       "\n",
       "         createTime      creator    last_resp_date  \\\n",
       "0  2018-12-14 08:38    greatbear  2018-12-14 10:14   \n",
       "1  2017-12-23 18:44    yyttcc705  2018-12-14 10:06   \n",
       "2  2018-12-17 15:21         共克时艰  2018-12-17 16:22   \n",
       "3  2018-02-27 15:18  chenjie2002  2018-12-14 10:32   \n",
       "4  2016-12-19 17:06      zhjienc  2018-12-17 16:27   \n",
       "\n",
       "                                                resp  resp_no  \\\n",
       "0  [{'seeker24680_0': ['0', '可以说很多不明觉厉的词，适合用于忽悠别人...       11   \n",
       "1  [{'xsxinyi_0': ['0', '你好，问一下，雅砻江的年报哪来的？公司官网上好像...       42   \n",
       "2  [{'z383788052_0': ['0', '我介绍个妹子给你服务一下']}, {'风呼...       11   \n",
       "3  [{'收垃圾债_0': ['0', '违约债券按5折赔付？ 想的太美']}, {'shshc...       35   \n",
       "4  [{'FZ3600_0': ['0', '511690 511700 511850 5118...       22   \n",
       "\n",
       "                      title                                    url  \n",
       "0          各位研究技术指标有什么心得体会？  https://www.jisilu.cn/question/297952  \n",
       "1                  雅砻江水电再探讨  https://www.jisilu.cn/question/259131  \n",
       "2                 散户是弱势群体……  https://www.jisilu.cn/question/298174  \n",
       "3  截止今日2.27持有的全部债券，加了杠杠0.6倍  https://www.jisilu.cn/question/265716  \n",
       "4    有人总结华泰货基赎回的坑了，我来总结一创的吧   https://www.jisilu.cn/question/82064  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 124855 entries, 0 to 124854\n",
      "Data columns (total 9 columns):\n",
      "content           124827 non-null object\n",
      "crawlTime         124855 non-null object\n",
      "createTime        124827 non-null object\n",
      "creator           124465 non-null object\n",
      "last_resp_date    124855 non-null object\n",
      "resp              124855 non-null object\n",
      "resp_no           124855 non-null int64\n",
      "title             124855 non-null object\n",
      "url               124855 non-null object\n",
      "dtypes: int64(1), object(8)\n",
      "memory usage: 8.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['createTime']=pd.to_datetime(df['createTime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 147074 entries, 0 to 147073\n",
      "Data columns (total 9 columns):\n",
      "crawlTime         147074 non-null object\n",
      "createTime        147074 non-null datetime64[ns]\n",
      "creator           146664 non-null object\n",
      "last_resp_date    147074 non-null datetime64[ns]\n",
      "question_id       147074 non-null object\n",
      "resp_no           147074 non-null int64\n",
      "title             147074 non-null object\n",
      "update_time       147074 non-null datetime64[ns]\n",
      "url               147074 non-null object\n",
      "dtypes: datetime64[ns](3), int64(1), object(5)\n",
      "memory usage: 10.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['last_resp_date']=pd.to_datetime(df['last_resp_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['crawlTime']=pd.to_datetime(df['crawlTime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.set_index('createTime',drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crawlTime</th>\n",
       "      <th>creator</th>\n",
       "      <th>last_resp_date</th>\n",
       "      <th>question_id</th>\n",
       "      <th>resp_no</th>\n",
       "      <th>title</th>\n",
       "      <th>update_time</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-06 13:49:00</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>缓慢投资</td>\n",
       "      <td>2019-12-16 12:41:00</td>\n",
       "      <td>340297</td>\n",
       "      <td>10</td>\n",
       "      <td>为赚66元的期权的小小套利</td>\n",
       "      <td>2019-12-18 20:57:19.897</td>\n",
       "      <td>https://www.jisilu.cn/question/340297&amp;sort_key...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-06 14:15:00</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>HENINHAO</td>\n",
       "      <td>2019-12-07 09:41:00</td>\n",
       "      <td>340299</td>\n",
       "      <td>0</td>\n",
       "      <td>谈妥了?降进口罐睡</td>\n",
       "      <td>2019-12-18 20:57:16.024</td>\n",
       "      <td>https://www.jisilu.cn/question/340299&amp;sort_key...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-06 14:34:00</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>88924835</td>\n",
       "      <td>2019-12-06 23:18:00</td>\n",
       "      <td>340302</td>\n",
       "      <td>2</td>\n",
       "      <td>不懂就问：如何开三个封闭基金帐号？</td>\n",
       "      <td>2019-12-18 20:57:16.198</td>\n",
       "      <td>https://www.jisilu.cn/question/340302&amp;sort_key...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-06 14:48:00</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>blsbls</td>\n",
       "      <td>2019-12-08 23:52:00</td>\n",
       "      <td>340303</td>\n",
       "      <td>8</td>\n",
       "      <td>WSJ: 中方表示，贸易谈判仍在正常轨道上。</td>\n",
       "      <td>2019-12-18 20:57:16.602</td>\n",
       "      <td>https://www.jisilu.cn/question/340303&amp;sort_key...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-06 14:48:00</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>甜橙飘飘</td>\n",
       "      <td>2019-12-06 21:52:00</td>\n",
       "      <td>340304</td>\n",
       "      <td>1</td>\n",
       "      <td>今天上市的 132021 中国软件 可交换债 19中电EB，感觉是个坑货！</td>\n",
       "      <td>2019-12-18 20:57:16.393</td>\n",
       "      <td>https://www.jisilu.cn/question/340304&amp;sort_key...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              crawlTime   creator      last_resp_date  \\\n",
       "createTime                                                              \n",
       "2019-12-06 13:49:00 2019-12-06 17:18:15      缓慢投资 2019-12-16 12:41:00   \n",
       "2019-12-06 14:15:00 2019-12-06 17:18:15  HENINHAO 2019-12-07 09:41:00   \n",
       "2019-12-06 14:34:00 2019-12-06 17:18:15  88924835 2019-12-06 23:18:00   \n",
       "2019-12-06 14:48:00 2019-12-06 17:18:15    blsbls 2019-12-08 23:52:00   \n",
       "2019-12-06 14:48:00 2019-12-06 17:18:15      甜橙飘飘 2019-12-06 21:52:00   \n",
       "\n",
       "                    question_id  resp_no  \\\n",
       "createTime                                 \n",
       "2019-12-06 13:49:00      340297       10   \n",
       "2019-12-06 14:15:00      340299        0   \n",
       "2019-12-06 14:34:00      340302        2   \n",
       "2019-12-06 14:48:00      340303        8   \n",
       "2019-12-06 14:48:00      340304        1   \n",
       "\n",
       "                                                     title  \\\n",
       "createTime                                                   \n",
       "2019-12-06 13:49:00                          为赚66元的期权的小小套利   \n",
       "2019-12-06 14:15:00                              谈妥了?降进口罐睡   \n",
       "2019-12-06 14:34:00                      不懂就问：如何开三个封闭基金帐号？   \n",
       "2019-12-06 14:48:00                 WSJ: 中方表示，贸易谈判仍在正常轨道上。   \n",
       "2019-12-06 14:48:00  今天上市的 132021 中国软件 可交换债 19中电EB，感觉是个坑货！   \n",
       "\n",
       "                                update_time  \\\n",
       "createTime                                    \n",
       "2019-12-06 13:49:00 2019-12-18 20:57:19.897   \n",
       "2019-12-06 14:15:00 2019-12-18 20:57:16.024   \n",
       "2019-12-06 14:34:00 2019-12-18 20:57:16.198   \n",
       "2019-12-06 14:48:00 2019-12-18 20:57:16.602   \n",
       "2019-12-06 14:48:00 2019-12-18 20:57:16.393   \n",
       "\n",
       "                                                                   url  \n",
       "createTime                                                              \n",
       "2019-12-06 13:49:00  https://www.jisilu.cn/question/340297&sort_key...  \n",
       "2019-12-06 14:15:00  https://www.jisilu.cn/question/340299&sort_key...  \n",
       "2019-12-06 14:34:00  https://www.jisilu.cn/question/340302&sort_key...  \n",
       "2019-12-06 14:48:00  https://www.jisilu.cn/question/340303&sort_key...  \n",
       "2019-12-06 14:48:00  https://www.jisilu.cn/question/340304&sort_key...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3039"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['2019-11'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2675"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['2019-10'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['2019-12-18'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-21afa84c4c62>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnew_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'W'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "new_df = df.resample('W').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'new_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-410704981cfa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnew_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtail\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'new_df' is not defined"
     ]
    }
   ],
   "source": [
    "new_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_df = new_df[['title']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createTime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-06-21</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-22</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-23</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2012-06-24</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-25</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2012-06-26</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-27</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-28</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-29</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-02</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-03</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-04</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-05</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-06</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-07</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-08</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-09</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-10</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-11</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-12</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-13</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-14</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-15</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-16</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-17</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-18</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-19</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-20</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-19</th>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-20</th>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-21</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-22</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-23</th>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-24</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-25</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-26</th>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-27</th>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-28</th>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-29</th>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-30</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-02</th>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-03</th>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-04</th>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-05</th>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-06</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-07</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-08</th>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-09</th>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-10</th>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-11</th>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-12</th>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-13</th>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-14</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-15</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-16</th>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-17</th>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-18</th>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2737 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            title\n",
       "createTime       \n",
       "2012-06-21     19\n",
       "2012-06-22      7\n",
       "2012-06-23      1\n",
       "2012-06-24      0\n",
       "2012-06-25      3\n",
       "2012-06-26      2\n",
       "2012-06-27      6\n",
       "2012-06-28      2\n",
       "2012-06-29      5\n",
       "2012-06-30      4\n",
       "2012-07-01      0\n",
       "2012-07-02      6\n",
       "2012-07-03      4\n",
       "2012-07-04      1\n",
       "2012-07-05      5\n",
       "2012-07-06      3\n",
       "2012-07-07      0\n",
       "2012-07-08      3\n",
       "2012-07-09      4\n",
       "2012-07-10      3\n",
       "2012-07-11      5\n",
       "2012-07-12      7\n",
       "2012-07-13      0\n",
       "2012-07-14      0\n",
       "2012-07-15      3\n",
       "2012-07-16      1\n",
       "2012-07-17      3\n",
       "2012-07-18      3\n",
       "2012-07-19      5\n",
       "2012-07-20      1\n",
       "...           ...\n",
       "2019-11-19    107\n",
       "2019-11-20    125\n",
       "2019-11-21    132\n",
       "2019-11-22    118\n",
       "2019-11-23     63\n",
       "2019-11-24     61\n",
       "2019-11-25     89\n",
       "2019-11-26    126\n",
       "2019-11-27    144\n",
       "2019-11-28    125\n",
       "2019-11-29    136\n",
       "2019-11-30     59\n",
       "2019-12-01     65\n",
       "2019-12-02    111\n",
       "2019-12-03    133\n",
       "2019-12-04    124\n",
       "2019-12-05     93\n",
       "2019-12-06    100\n",
       "2019-12-07     61\n",
       "2019-12-08     67\n",
       "2019-12-09    105\n",
       "2019-12-10    115\n",
       "2019-12-11    128\n",
       "2019-12-12    128\n",
       "2019-12-13    123\n",
       "2019-12-14     57\n",
       "2019-12-15     80\n",
       "2019-12-16    138\n",
       "2019-12-17    175\n",
       "2019-12-18    151\n",
       "\n",
       "[2737 rows x 1 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14ae52929e8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\byb\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n",
      "will be corrected to return the positional minimum in the future.\n",
      "Use 'series.values.argmin' to get the position of the minimum now.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-10-07 00:00:00', freq='W-SUN')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_df['title'][-:].argmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "sort_df = plot_df.sort_values(by='title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 51 artists>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14,4))\n",
    "ax=fig.add_axes([0,1,1,1])\n",
    "ax.bar(plot_df.loc['2019-01-01':].index,list(plot_df.loc['2019-01-01':]['title'].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14ae47f75c0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8I3kfwIuBdwE3mNm8GPaPwBzgB2Z2GHA7cFCMuwDYD1gMPAq8dwR5CyGEGCG1BwB3/w3peX2AvRPnO/DBuvkJIYToL9oJLIQQLUWvhOwRvZZOCDHR0B2AEEK0FA0AQgjRUjQACCFES9EAIIQQLUUDgBBCtBQNAEII0VI0AAghREvRACCEEC1FA4AQQrQUDQBCCNFSNAAIIURL0QAghBAtRQOAEEK0FA0AQgjRUjQACCFES9EAIIQQLUUDgBBCtBQNAEII0VI0AAghREvRACCEEC1FA4AQQrQUDQBCCNFSNAAIIURL0QAghBAtRQOAEEK0FA0AQgjRUjQACCFES9EAIIQQLUUDgBBCtBQNAEII0VIGPgCY2T5mttDMFpvZ7EHnL4QQIjDQAcDM1gH+E9gX2AV4u5ntMkgNQgghAoO+A3gBsNjdb3H3J4AzgQMGrEEIIQSDHwC2Be7o+H9JDBNCCDFgzN0Hl5nZQcDr3P3w+P+7gBe4+4c7zjkCOCL+uzOwsCOJLYC/JJLOhTfBpgka2qS7TWVtgoY26R5PZX26u0/PnLcGdx/YAfwtcFHH/58CPlXBfm6V8CbYNEFDm3S3qaxN0NAm3eO1rGXHoKeArgZ2MrPtzWwKcAhw3oA1CCGEACYPMjN3X2lmHwIuAtYBvuPuNw1SgxBCiMBABwAAd78AuKCm+ckVw5tg0wQNbdLdprI2QUObdI/XsmYZ6ENgIYQQzUGuIIQQoqVoABBCiJYy8GcAvWJmRtg5vC3gwJ3A72L0sHB39xKbpwD7FMIvcvcHYl5bdsa5+z25tGI+m+TSK7OLeW0OuLvfXyhvJQ05m7LwOnF1NNSpu/g5V6c5mwer9oUuGrLp1Sxr5f7Yrb6jjrX60Ci0UVlcrs+VXRN12nwg139Nm2cSPBh0xp3n7gv6XNadS/LJaqACjXwGYGavBU4EFgF/jsEzgOfEz9cXwncEvg0cnrDZHVgFnFsIfw3wXeANwCaFOAemAjcm8vkR8BbgZ4n0zgHenNDwTOA24NnAA4AROtilwOnAsRU1fBl4T8Lmifh53UL4A8DXgI8mbMri6mioU3dvip9/xPA6vRh4dcJmf8JKsmsT+eT6QpmGsvTqlLVOf8z1nx2BTwNvBPZm7T40H5gJLBhl3bsAj8T/i/3n/JhP6prItV9Zm+fqod/Xfy6fMpu5hC/mMwmeDIbiDiFsWp3Vp7LOIvwg+VYin7uArTMaznT3OfRK1Y0DgzgInXlmInwxwZdQMXx74PGMzS3AwkT4ZsAKYK9E3K3A/JJ8Ns2kl9NwbWyodTrC1okN9kgNDTndNwOLEuEvBB7L2JTF1dFQp+4WZdp1M8KglrJZCNxSsS+UaShLr25Zq/bHMt2PAgcn+tCfgesGoHs+cGum/6wouSZy7VfW5jkN/b7+69g8AaybiJvS57LeAiwoySenYdj1X3Y09RnAZNaMbJ3kblf+TPhFlLJZSfhFXGQ1MMndr8rYTCnJJ6VjdYmGjYEV7r5qKMDdV7n7mYSGrKohp9visRbufiXhi2OYTVlcTQ116i6pm1CnZGwmE36l5fJJtUOZhrL06pa1an8s0z3Z3c8q9iHCwDB1ALrXJVE/sf9MIn9NkIkra/Ochn5f/3VsALZJxG1dorFOWZ8ANsjk4yUaVifCszT1GcB3gKvN7EzWOI/bDtgIwMyOKYQfQphOSdmsB0w1s5M6wp9GuP36pZmdT5iG6bRZBUzL5HMucK2Z/SyR3rkZDVsAC81sr0L4ocCfami4OmMzOVSPHVwIfzewIGNTFldHQ5262zjqTrXRaRmbTYGHK/aFMg1l6dUpa53+mOs/hxDa6MRYH51x9wE7D0D3JsCyTN/6VaZOy9qvrM1z9dDv6z+XT5nN14FLzGxRIW5H4IQ+lnUysLGZ/TSRz2dKNHyICjTyGQBAfE/A/oSHHEOj5JDbiGHh7j6/xOYu4HWF8Ivc/X4z25c1D1M6bW4ryWezkvRSGn4KvCiTzynAq2poyOn2VLi7X5CzKYurqaFy3cV2zdVpzmbrqn2hi4ay9OqUtU5/zNksBg7LaLic8I6N0db99FRasf+UXRN12nxQ138dm0mseWg7FHe1u6/qc1n/UJJPVgMVaOwAMERxxUO38FxcbvXCCPIvTa9MX1X6mdYgNVStu7I67RJXqS+MJL2qZc3FjUb/GYTuLmlVbr+69TCI+i7R3PfVeSkNZfl009ArjRwAzOxpwL8Rfhk/GIM3Aa4kjHZ7FcIvJdx+HZWwuRbYElifMEoaa1YvfBLYj/Cr5qnR5l7gsmjz0kQ+nat2iul9hnBLXNRwGfD7mF5ng50L/BD4h4oaPg+8M2FzYfy8TyH8XMKqqiMTNmVxdTTUqbvO1UvFOu1codQZ9yiwlLBio9e+UKahLL06Za3TH3P951LgnwiraQ5k7T70K2A34JWjrPvX0fYVDO8/5wNfStRpWfuVtXmuHvp9/efyKbM5HTia9EqtzlVXIy1r2equslVuR7r7z+iVKk+MB3UAV5Be8XBzLHRqNc3yjM1twI2JPF4IPAQcA2zVEb4V8CfCUrMqq3ZeGONSGi4H7onnzIjHC4GTCLeaVTU8kLEZqp9i+GyCn/CUTVlcHQ116q7O6qUbYttW6QtlGsrSq1PWOv0x138OiW10UqIP3Q38dgC6fw/cnuk/yzN1WtZ+ZW2eq4d+X/+5fMpsVpBfqVVndV5OQ9nqrrLVYsNWDpUdTb0DWOTuO6XCATJxT7j7sJUNI7BZ7e4792rTJb2FhBUZo6oh5kMfddfRUNfG3H3H0bQZJQ11ylqpP/aQnrv7M0ZZd+U+3IOGOvU9qOs/awNs6O4rC+FTgIdHu6xD+ZRomJ/KJ0dTVwFdY+kVDxCmxlKrae7O2DwGbGHp1Qt3m9kngdN87Xm6RwirJKqs2nk3cFtGwybALWY2yd1Xx3wmAQcBj9bQsCxjY7F+tiyEvwdYnrEpi6ujoU7d1Vm9tBFwX8W+UKahLL06Za3TH3P959DYRgcB5xT60L3AjgPQvWFML9W37i65JnLtV9bmuXqI2Y56fZfZXEN+pVad1Xk5DWWru8pWuZ1CBZp6BzCF4Sse7mCNG+nXF8J/TKj0dyVsfkyYd9uP4U/ZryDcwh5AmPNzwlTNTwi31q9JpFW2aufijO7LgecR5k+HHjJtSpiL/QLwjooaziHMQxZtLornva4Qfh7wTeD9CZuyuDoa6tZd1dVLF8T/U+1d1hdyGsrSq1PWOv0x139+HOM+G7V39qFfEqZnXjLKui8mrJJ6A8P7z/GEefmqK8ySbV5SD/2+/svqO2kTy/OsTFkrr87rouE3GQ3zyzRQgUYOABMZM5tGqPfcez2FKEV9SPSLpu4EfhIze0Pq/1x4F5sjCuFHdHzeoxC3Rw/5lKWXs1u388I1s61GqCFnkwyvG1dTQ+W661KnOZvKfWEE6dUpa53+mE2PTB8aoO6y/lOn/SrXw6Dqu4vNcYW441Lndf5fU0NZPtm4Xmj8AADsmfk/F14WZ4Xwzv8/UIgb+r8sn7L0cnbFObrO/+toyNnkwuvG1dFQp+7K6jQXV6cv1E2vTlnr9Mey9HJ9aFC6y/pPnfarUw+Dqu8ym2sKcddkzuv8v46GsnzK4rqiKSAhhGgpTV0FhGX8XbP2w5Qnw73ERzbBT0ZxA8257n6h5f13b1WSz+tK0stp6NzWXdzVV0dDHb/jSZuyuJoaKtdd/Jyr05zNbVX7QhcNZenVKWud/limO7n7k3K/8f3UfWdJ/ym7Juq0+aCu/0o2rHlo+yaCQ7bOuKEFBv0o6/nAizP5nEpY+ZTU4O5/pUcaeQdgYenT2xnu7/qo+PmEQniZj+yPE5ZUHVcIfzdhw8WODPfffRBhre1JiXweJmwgOz2R3lMInhmLGt5HWAp6NcN37uX805dpqON3POeXvSyujoY6dffF+PlTDK/TrQltW7Q5DpgG/HsinzJ/6TkNZenVKWud/pjrP4cQbu1fTjW/8f3U/YGo7QcM7z+LCQ7mUtdErv3K2jxXD/2+/nP5lNnsQFiGeVoh7lDCl/8f+1TWYwjt99FEPvsRVkSlNGzu7gfTK1V2jQ3qIOycS/m7zu2oK/ORnbMx8v67FwM35/LJaLYSDQtI+1If2tVXWUPGZhHV/eqXxdXRUKfuStuook1ZX6ijoXZZa/bHMl/zMxNx3fzG90v3QuCPuf5Tdk3UafOKddfv+q7cH2N8P8u6MGVTls9QXrm41NHUh8CrSfu7nkT6wXWZj+xVpH2f7xltUrdAq0n7A98aWG1mL8ikl9O9Pmv8gXTS7f0COQ1kbIzhD5mG0srZdIurqqFO3eV07wmsythMIe2/v6u/9BrpQfWy1umPuf4zpGFJIq7Mbzz0T/e6pH3Nrwa85JrItV9Zmw/q+s/lU2az0swOsrAJDwAzm2Rhk9fKPpb1EWDDTD6Pl2io5DCyqc8APkra3/WGAFbNR/ZU4BEzm8+aC2g7wjTAF0j7754KrJPJ5+PA181s40R6n8hoeApwv1XzkV+moY7f8dMyNmVxdTTUqbsVBOGpNjq8xGajin2hm4ZcenXKWqc/5vrPjsB/U91vfD91b0T4Mk/1rS9k6rRb++XaPFcP/b7+c/mU2byNsOHsRDO7n/DFvilhWuhtfSzr5oQ7tXsS+ewLfDij4RAq0MhnAMDQNvdh/q4JI31lH9kW1ko/Ge7ud8d8cv67H8ylFe1y6eV070x1//RZDSU2pMK9xC97WVxNDXXrLhmei+vS3qX+0mukV6d+6vTHMps6fuP7ppvwQybZf+q0X5fwQV3/lW06ypTckNevsnb01ezGv7K4XmjqHQCsmZ5x4m0ma09XlIWvFRdXQrycjqfsZnaRuz8Qv/wu64yLYZbLpyy9nAYPW7TnW8LveB0NOZuoLxleZtNPDXXqLn5O1mmJzYNV+0IXDdn06pQ1p6FO/4kakn2o322U09Clz2XLVKfNy+phEPXdpTxrrdoxs6EVPX/oZ1m75JONowKNvAMws9cSfNQXVzw8J36+nt59ZO9OmNM7l+GrF75L8G1S9N/thNvAGxP55FbtvIbge+XNCQ3PJCwx3IXe/dOXaajjdzznl70sro6GOnVXZ/XS/oRVXNcm8inzl57TUJZenbLW6Y+5/rMj8GngjVTzG99P3bsQ5qVheP85P+ZTZYVZWZvn6qHf138unzKbuYS7+dRKrTqr83IaylZ3la1yO9Pd59ArVZ4YD+ogdOaZifDFpFe5DK2mSdncAixMhG9G3n/3rQS3qrl8cqtpchqujQ1VxT99mYY6fsdzftnL4upoqFN3dVYvLQRuqdgXyjSUpVe3rFX7Y5nuR6nuN76fuueTXsk25CO/6gqzsjbPaej39V/HpttKrX6VtdvqrpyG5Mqh3NHUVUCTSa94yN2uDK2mSdmsJL3iYTXBv/lVGZvUKoBuq3ZyGjYGVnjH+zrdfZW7n0loyKoacrqTqw3c/UrCF8cwm7K4mhrq1F2d1UuTSa/aKesLZRrK0qtb1qr9sUz3ZHc/q9iHCAPD1AHoXpdE/cT+M4n8NUEmrqzNcxr6ff3XsYHylVr9KmvZ6i4v0ZBaqZWlqc8AvkN6xcNGwNBGsV59ZK8HTM2sXvilpf13ryL4S6+yauc1MS6lYQtgoVXzkV+moY7f8Zxf9rK4Ohrq1F2d1UubAg9X7AtlGsrSq1PWOv0x138OIbRRVb/x/dS9CeF9AKm+9atMnZa1X1mb5+qh39d/Lp8ym6+TX6lVZ3VeTkPZ6q6yVW4fogKNfAYAYBl/14TRL+eLO2dzF/nVLzn/3beW5FO2mial4afAiwrhvfjIL9NQye+4l/hlL4urqaFy3cVmr7pCaauSfMp8ttdJr05Z6/THnM1i6vmN76fup5f0nzorzMrafFDXfx2bOqvF6pS1bHVX6cqhXmnsACCEEGJ0aeozgCexjL/rXHgXm5ML4Sd3fM757y7Lpyy9nIY6/unLNPTNr/4oaCizSdZdlzrN2ZTlU0dDmU2dsibj6vSf+LmO3/h+6i7rP3Xar851VEd3nXzKbH5SiPtJ6rzO/2tqKMsnG9cLjR8AyPu7ruMj+78K4Z3/Fx/SDP1flk9Zejm7Ov7p++13vE6yZ4y1AAAgAElEQVRcHQ116q6sTnNxdf2l10mvTlnr9Mey9Or4je+n7rL+U6f96tTDoOq7zOZ9hbj3Zc7r/L+OhrJ8yuK6oikgIYSogSU2dQ46n5FqaOQAYGaTSfvc/jFrHgL16iP7ovj//sD0mMW90WYO4WXWRT/dPyY8iU/52/4h8A/Rppjel4C3Zux+w5qXWQ+Fl/mnL9NQ1+94VZ/tdTTUqbsL4+d9E3V6InBkwmboQd0+iXxOJe8vPaehLL06Zc1pKOuPZf3nFIIr4ip+4/up+1zgdsJmtGKfu4Lg6jh1TeTar6zNc/XQ7+s/l0+ZzX8D/wzsTdgEZwQXGZcCnwfe2aey/grYjdB+xXxOILjGTmmY7e630SNNXQb6PULBjmPtnW7fIxT2/xXCDyVs5LkgYfMDgoe8V/jaPjkOBX4P3MRw/93fBv4CHJHI59OEnbOp9G4C/jeh4asEn+DHE17gMRT+fTPL+acv03Ajab/jJ8XPRb/jR5nZF0n7ZS+Lq6OhTt39itCuL03U6bWEi6RocyHBf/9BiXxyfaFMQ1l6dcpapz/m+s+hhB2oTtj92dmHfk3eb3w/dZ9MWM58dCH8KMI0xomZMuXar6zNc/XQ7+s/l0+ZzW+Avwfe6Wt89axD6De/Jlzn/Sjr/xE2g22VyOciwq7nlIYzCZvzeqKpdwAL3X3nVDhB8zMScU+4+7DNLSOwmeTuO/Vq0yW9mwGKGsxsCvBwvzTEfKxoY2YGPJ6xKYuro6GuDZk275vNKGmoU9ZK/XEoDtjIC297GmAb5fpwtv/0oKFOfQ/q+q/VRn0s66JgUlnDolS75mjqQ+D7LeHvmqDXiuFW4iObsE19fTPbsiN8SwubSR63tP/uJ4ApmXweNbNPZtJ7LKNhI9b4UemkzD99mYY6fsdzftnL4upoqFx3UfOkTJ0uz9hsQGi/qv7ScxrK0qtT1jr9Mdl/YnqrqO43vp+61yP9Tos9o03umsi1X1mb566jfl//uXzKbJaZ2YlmtpeZbROPvSxs0lvWx7LeC2ySyefuEg3XJdooS1OngA4hTJcU/V1fGT9X8ZH9a0Jl/rKjAe4mzPm+kbz/7oWZfF4OvCOT3suAf0xomA883ar7yM9pqON3/PCMTTef7XU0VK27C+J5qTqdBbw/EXc+YeNWVX/pOQ1l6dUpa53+mOs/l8Y6qOo3vp+6rwW2zPStN0a7Ku1X1ua5euj39Z/Lp8zmeQTnbf/K8I1b/0qYIutHWX9B2ECayueThHcSpOJOoQKNnALqxPI+t/vmI9vK/ZjX8rddtLMa/um7acjZdEmrblwlDSOpu6r0sy+U2dQta7809NCHRl13WVqjwSCu/7o2/WYsNDT1DgDL+LuO0ZV8ZBO2sOdWv+T8d29Vkk/ZapqchoVk/JjX1NA3v/plcTU1VK47aqxeIrjYruwvvWZ6dcpapz+W+Xl3En2oS1n7qfuukv5TdYVZtzYf1PVfyYbg3vqwTFzd1XkpDT8BXpJJ61TCA+SkhuJzojIaeQdgYW7s7Qz3d31U/HxCIbzMR/bHCQ6zjiuEv5vgTndHhvvvPgh4mLCqpphPbtXOuwlLsaYmNLyP4Ezraob7Mc/5py/TUMfveM4ve1lcHQ116u6L8XNx9dK7CW2aWqF0HGHVzr8n8inzl57TUJZenbLW6Y+5/nMIYVPQy6nmN76fuj8Qtf2A4f1nMeEZReqayLVfWZvn6qHf138unzKbHQhTTqcV4g4lfPmnVl3VKesx5Fd37UeYVkpp2NzdD6ZXvILv6EEdBL/2KX/XOX/3U8j7yM7ZGHn/3YuBm3P5ZDRbiYYFpH2pb0/eP32phozNIqr71S+Lq6OhTt2VtlFFm7K+UEdD7bLW7I9lvuZnJuK6+Y3vl+6FwB9z/afsmqjT5hXrrt/1Xbk/xvh+lnVhyqYsn6G8cnGpo6mrgFaTXvEwtBKgSJmP7FWkfZ/vyZrb6VT+KX/gZat29iSve33SKyi6vV8gp4GMTW4VUJlf9m5xVTXUqbs6q5emkPbf39Vfeo30oHpZ6/THXP8Z0rAkEfcEeb/x0D/d65L2Nb+aMG1RdYVZWZsP6vrP5VNms9LyK7XqrM7LaShb3VW2yq3SjuCmPgP4KOkVDxsCWDUf2VOBRyy9euELpP13TwXWyeSTW7XzEPCJjIanEJa2VvGRX6ahjt/x0zI2ZXF1NNSpuzqrl1YAG1XsC9005NKrU9Y6/THXf3Yk7ECt6je+n7o3InyZp/rWFzJ12q39cm2eq4d+X/+5fMps3kZYgTO0agfCqp3LqLc6L6ehuLqrM5/iiqfOuEOoQCOfAUB+xQNhpK/sI9vyK2Zy/rsfzKUV7XLp5XQ/g+r+6bMaSmxIhXuJX/ayuJoa6tZdpRVKXdq78qqrLunVqZ86/bHMpo7f+L7pJvyQSfafOu3XJXxQ139lm44y9WV1Xg99ta8rntayb+oAIIQQYnRp6jMAIYQQo4wGACGEaCnjYgAws6eY2fPjfGYxbvPC/1ua2R5mtnv8vOkI8l0rrRhWKT0z27/CuZunytij7R517PqZnpltMcI8p8e6fo6ZTc2cs6OZvcXMdukxzc3iQ7lcfDa9Yt8aChtBG5X2BTObasEVeuf/s1I6YvxTa2gYURv1mlZOc+K8Xtp8YNf/UHoW2MvM3mxmb4qfzcyeWzG9I7vE99TmMXyPzvKY2bCVXJXbt8qa0UEdhBUPW8TPryM8Hb+Y4D9jCcGF6l7AzwnroO8gbKq4krDm/uJ4DO2evIqwe2/TQj7PJPjbOJ+wweNUghvqG4F5ibSuBFayZjdgMb03F463RM1/R3A3/J+EzUbHATcQNtXMImwCWUpcx0/wQ/J4DN+b+KymI589CsfzY73sDuyRqdMHCX7Md0jE1Unv1nj8Jp53E2ETzDJg73jODOCSWKe/Bd4Q6/AOgnvhzeJ5u0R9iwnLGq+KaZ9KcKU71BfeRVhT/e3YHtcQ/Lb8Ix1rqWNdnx7TXEXwY397rPdfZNL7Y2yrVN86sEYb5frCm4E3Z+r0vnjcTFjpcUusvzsIq2k27zimEXYub0bY/NOvNnpGJq19Y9sU01oCfKfjvF2i/lujvnfUaPOzGMz1/+J4fjG9e2M+P41949uEPjW04W0x8Flgl0J6RxeOjxPcbh8NHF2xzZcDb4/nvITQfy+LZZ0d9S0lbPKb2ZHetZW+a8f6yz5TKTd0fP7tUAEJDqkWAn8bK/YlHV9gDwN7JdJaHCv1f2JFn0tYKrUB4cvljYRdx3+K4RbPvzqR1guBxwhfZKn0VhK2cH8H+G48lsfGuiI23PWEXX5PIyzlWgYcDKzTkc86hK3dtwKXE/YL/Afwwhi/OtbLZR3HY4RVEr8j/YW+Evhy7Ei/Az4GbNMlvRvikfoyWwk8K7bFfR3a5g91QsIA9/eEO803Eb5k9iEsWfsE4cLbgXDhzo82LwBOi5/fBzzYUS9XA9Pi50tive4GfD3qH4pbTvDJTtT7VcIyxs8ByzLpzSVchKm+9VCNNsr1hd8RvpBSXxYrCT73t4957hDT2pLwRXZr4fgrcA8dA0sf2uj6RFpvjvktS6T1LODRjno5H9i3oy0fGmGbj+b1/zvgOYn0bgXmJtLanrCkc1fCy18WE94pMhuYGdv3LOBYwnsnPk1Yl39hPKq0+fXA9fHzZcQfYsDfEPYIPDv+/1bCj5Kh9riuyndtI1cBmdlNwN+6+0Nm9hvgZe6+2syuA6a4+7PNbIG7P6vDZoW7r59I61rgKe6+o5ltQPjCP4Swrd7cfeiWb7G77xg/LwKWu/uwaZDOfBLpXU3YMHM28E13dzO7FXjA3XePNre7+9M60nvc3dfL6N7Y3Xcys6fFPA4hXEjzCL8Aj3f3C+L5txIGlV9CctPJy919Ujz3pYRB782EX0ALCRdyMb0ZhAsn1Une7e7rxHPvcPftOnRPcvfdzGyeu+/WUaZH3X3Djv9fSfhVSGd9m9m1HZ8fA3Z09z+b2WWEL5cVZjaP8Kv/2fG8/0fYar8/4QdEZz7XuPvz4+cVhIusmF7dvpVro1/G9ij2ha0JL0hZmajTYzvq9E53f3KDkAX/MNcD/+DuN4xiGzlhX0gxvf1j/WzcmVa0ebJdzey6ob5ejIv/99LmK4CnDvj6fzK93PVv4f0dD3XmY2Hj1yEE9xr3EO56/gj8q7s/ama3EDZ6VWrzqHuyuz+3s//GuMfcfYOO/59NcP8yG/iX1PdWliqjxaAOwoaKawhTJ8cD5xBu8ZYB/x7PObBgcx/h18fBwIvicTDhNvMbiTw2Ae7o+P/Ijs8nEEbzYlrnA0szmjch+OKYBHyEMGq/gPDr4/cd532uYPcA4W1Ke8WOsk38vBT4QSKfnQm/LKYSftn+kPBFcwth6mqnjL5h28cJv2L3Ifw6TaV3DbBrJr0VhF+O/0D4YvoYYR3zQ4Rfxl8n/CrunJp5DNikkM5zCb/eHo31/GXilAJhML2D8KvxM8A3CL8Ij402swtpvZrwq+xxwlujtiHcZZ0T460kveXAJzJ9q24bpfrCb4HnZ+r0MYLfmG8Q/M38O2Ga4tOEtfszYvt8hbDpb7TaaFh6Uc+yRFqHEr7YziO8snEpsOEI2/xOBnP9397x/4Ednz8VdR9DmMJ6R/x8HXBnpq6N8CMLwl6Nywm/zuu2+ROEPnlD/Ds0dTYp2mxVSGcG4Yfh8irftY28AwAws50I857PIOx2XEK41f2Suz9aOHcHwi3vDQzfKDPZ3Q/P5PH3wP+4+8OF8B0JI/Y9DN90s4u7f7kH/dsSvlBnEZ5p/Fsmn+MJ85Wduu8gbEXfz90f75LPbjGfZxPevXqDuy9MnPdrd39pD7o703sL8Cd3vz1x3usJvwpXE/ySv50wz7qSMC9+Vzz1PA8bzbYCvgnMcfcrC2ntSvgVewfhlnqOuy+34MnyWYS7lHewdl/YFLjQ3X9ZSGt3wgB+F2GeeR7hV/NdFjbNvIJQ38X0HiJMQ6T61tsIg0Cxb61Lb220DeE1lLMI87zL3H1p4ryhvJzwhfA64L2E6cnPuftd8bz9Cc89ZhJ+efazjb4E/FMxPTPbLpZhKeFZylBaf4ppdZ5/jbs/bGHhxBzgv2q0+X2M/vW/P3BxJr0PEKaFitf/bu5+Riq9QhobEup8L8K0Vq9tvg/wHsJgdwKhviEMPH+ND3k/BPyvu/++kNYmwIfc/fPd9D1p09QBQPSOmRlhKuKhJqYn+k+cztjB3W8cay1i/NLIASAui8r63PaEv2sz+xbhQd4M4Kfu/tsYvg5hfux6wi/GyztsjiX8yn8T4ba+l3xOdvcjMro7NRTzOpVw6zbMH3hJPRzr7p9JhRMeJM4ALnH322K4EaYqLiXMPb+K8IvoD4Rf368h45O8RMPphPnsJ/OJ4X9HmD4oaphM8Iv+1GI+lPgqz9VrSbtOJkwNrMfwtjuViv7SYz/5RTyKbfevhAdvTpg2OZjwi3Mh4RfsfgkNdfL5F8IKlFT/+WfCM6ae2y/Xf2Jcrl0PJ0wNFq+JH8fP+xfy/z/C9E2uXZPXROG6fLJdh3QTrsvRvv4/S7iLOxCYHoPvjfnMcfcHEvn81N33zZT1Z4SpqyENZ8TwTQjTgBsm8jmRcOd+IOGa8R40JPOJcSe6e+nS07WoMl80qAP4PsFX+QtjIWfEz98B/pe1l8MNLYl7BDiD4EjuGuArMa1vE26n1gqPcctq5PPnRHg3DV8jzEUeQljS9ZL4+QLgP0rq4fZM+IOEFUxfIzxw+nAMP5Gw6uA8wrTTDwlzp2cS5i8vqKKB4ORrRTGfGHd3RsP3CbetxTo9KVOnmxNWhaTqtaxOy/L5U6ZdyzR8jzAnneon9xPmZk8krD76BuFVfkMP0PuVz19SZY1x99Zov1z/+WJJu+auiaHlr1XKOsjrsk4+DxHm9rfqCNuKsJrrStKr6ZYmwofiHiNMeR1IuAaHfqBcROjfxXyOiW2e0nBCiYZkPtG20jLQpt4BLHT3nRPhqwjrcP/cEeyEObqn+5pVLpMJF+sWwE7ASnffvRD+dsJys9TKgbJ8ZhKWiVkiLqfhOcDD3rE6Ip5jhPnY1AvjhzYvLS/KIzywXdfdV1rYGHIG4Yvo1TG9FxC+oLd29yeilkc8vdqoTMNGgLv75M583P1jFlbnbJzQsB9hWeDuxcTiCpNU3W0fP99WCC+r011jmVL5POHuKXe+ZRpmEAo7JdFP7nf3DWNd3UWoVzezhcDj7j5sc1DNfB7wuLqjQl99iNBOxfYb6ifF/gPl7ZpbTbOQMGPwjAplHeR12e98VhCWiRZ5BeHXfOlKu5jOPxGuh6eSX1WY7KtdNOTy2R/4eSqfLFV/nQ/iIIx8BxGWqg2FTSJcfMl1rsBfE2HHEn5tLUqEX04YSSvnAzytooZHSPwaI3xRPwFsmYi7HfhzL/kQbtlPIfxSvSmGXVg45zHgBRU1LGDtlVJD+fyQ8MWX0nAfYVqkWKcHEzr0sLoj/LrMra7I1eny2E6pfJZn2rVMwx86y1roJ493hHVuerqSMGj1K5/USq0nNWTa724SL4SJcStrtOujmbpbFI+eyzro67JGPo8QXrC+ZUfclrFOL8/lQ36l3V87NcewQwkrnu5P5HMM4XqpoyGVz02EBQHDbHJHzycO8iD8yj6LcLt1czyWEm7h9s3YXA3skwi/gvALoBh+eKzIqvn8AHheRQ2fIazEmE/YufezeBFeRbhFTV3YnwNOz+SzmLjkLBG+OhG+FWGJ6FUVNfwEOCOjzTMavh7jinU6tEFmWN0BHwS+WLFOj4l1msrnJZl2LdPw38CJmX6yGpiaiHtZTLef+aTKOtRXU+33Z8J6/1Td/bZGu67O1N2Po13PZR3wdVk3n+MJg/KyeCwgPNcYVm/R7nhg50zcj4BXJ8LfGtMu5nM84e63qoZcPvuQeYtY7mjkFFAnNkJ/103Kx7r4F6+QzgYA7v5YIm5bd/9zIWwjYCN3v7eKhi757ED41Z7V0IS2G20NZmbu7gMs64j7UK/9J1emQZV10Hm1kiqjxVgewMlVwptg0yW940psknGDshkFDeO1jRprM+B+0qb6/kmV8Lpxg7LpdowLb6CRWRXDm2BTFlfmGTIXNyibfqc3XtuoyTYwuH7SpvretmJ43bhB2ZQyngaAeyuGN8GmLC61iqBb3KBs+p3eeG2jJtvA4PpJm+r7uorhdeMGZVNK458BTFTMbJK7r64SNyib0UhP9J+h5w+9hte1EROYOvNGY3nQ37nAbxGcZX0WeHFH+DqEnXhrhce4f0nZlMURdgBeSHCktT7B18d5wL8RdgZ+MhH3FYL//n7ZpFax3FxSZ8m4bjbAczv+XzfqOY+wqewFmbhvER2IVbC5CNguhu9I2JR2P2GlzBdY40++17jbgJdWtFlG8MCYqtsfERzSTe0lvIe4vyFshPocYX3/twgruy4grEwrhv8QeGkNm+0JTtjOJ/jouYawkfCVmfBXEGYSqsaVpbcJYaPTH1jjL38BwUfVVxPhcwgODKvabJrqx7G+f1olvIvNUwgb7r4HvCPxHfTFYly0uaaKTYwbtsKs9Pu0ysmDOkjvKuy2YzS3Q7fOLtOy3YNluzWTcYSL7R6G7yT9EuGLJ7XL9A+EJX/9svkrYefjQ4R18ssJm2qWE5ZtpuK8I65Xm1UEl7lEjacSXO9+Fbivo6464+4hLnmtYHNfh835wJvi51cw3D99L3G3EtddV7BZSmjzZbGN30RwVwyhz51djMuFl9nEuF8RHJTNJnxZfxzYjrD5bmEi/DCCA7uqNncSnL29hLBT+DMEFyJ3EgaOYvjFhP0QKZuyuLL0FpDeHXszYS9ClR21ZTapnbZ7AO8kveO3207gXNwlhL0AqZ2795PePXwOYS9AzzYxbkK8EGYVwY3qrR3HLaz5QiqG3xrDq9qs7shzMsFP+Y8IXgWvS4SvBzyWsSmL+z1hjs5iow5NvdmQTSJuHsF/Sb9s/kJ4S1bnhpNb49+vp+Jy4V1sruv4f8hn/1q6E3HXseblF73aLOywWevlPcCKjs89xdVJb0g3Ydf2uwhfaEsJ7rUXx3OKcfcBr61o82RcjL+9oOG6Ynj8/9EaNo8V/r8y/r2Bjg1nHeHrddZPhbiy9NbaZFhoo4WZuGGb6HqwcYLfrMsKhxO+g4rhl5XYlMUtZ+1+/E+EjWjTOtuoEHcDHV/mPdpMY4IMAIuosWO0jk0irNvuwbLdmrm4h4fSo2Mnafy/s2N07jKdR3yPQJ9sfk/4lXIpcBTh1vyWjvhkXFWbeLyJ4CxtQUHD46k41gzIVWw+T/il/DcE18gfJUwDvDe2+akV435M2MFcxeY2CsvvCHeX7yfhlz3G3Q5cWtHm/YQ7rWcAexIG81kx/kbCnV8xfEdCP65jM/RWqj2AX8XP17CmDz8ZPtQfUzZlcV3Se5j07tihX/NVdtSW2TxMYldvrJ+7Sr5nynYCp9Jba+d1DDuUsHM3t6v3cQq7enuwmTA7gT9I9R2jyR26XWzq7B4s262Zi/s16cFmB8Iv9dSc71nAb/to85v4eRLhC/vXFAbGXFwVG9a8/nDo2DKet1UifijuzEJcLzZbEaa7riJ8iS2P/3+BMIf8nhpxH6hoczuFl5101MuvqoT3ELc34dfsAsK0yTmEnd/3E6aOiuH3Ep5JVbX551iuRYRBea+Y/5sIg1AxfDpr3gdQJa4svf8gvTv2azGuyo7aMpv3kNjVS9i5e2SmHcp2AifjCM/gjk2E7xP1pHb1/pCw0a+KzcTbCTzR6feKjV5tzGxrYHePr4AsnJuMq2MjRg8LLwe5391X9RLeq010ejfNh+8ATobXjSuzEYNh8lgLyGFmz2TN232c8EvwvPh5WLi7Lxhrm9HQYGajamNmt5bFpXTXsZnIbTTGus8N36PDw4E/1LEhvNLyAAtvtRumuxju7gtyNmVxXdJLYmbvdffv9hreBJumaEie38Q7ADM7huCu9UzCq9gguNE9Kn4+oRB+CNFN7xjaNEFDm3S3qaxN0DBI3We6+xwSmNnt7v60XsObYNMUDcnzGzoA3Aw82wtv/onh5u47FcKnEB7obDSGNk3Q0CbdbSprEzQMUvdywrOLIjsRVgkVX4NphHdYp16POSibpmh4hife+5GjqVNAqwmvo/tTIXwSJLesb024hRxLmyZoaJPuNpW1CRoGqXsdwpvs7i/EzSUMDm8shBtho9VY2jRFw2+pQFMHgI8Cl5jZIsLSPAjL7jYEMLOfFsJ3JGwkGUubJmhok+42lbUJGgap+2LCKrd5dGBm5xGWehcHE8zstrG0aZCGXxTDymjkFBCAmU0ivK3qSd/nhGWbngqPqxfG1KYJGtqku01lbYKGQepGDAavsGZ0LA/giCrhTbBpgoY26W5TWZugoU26x2tZux2VDcbqILPFORfeBJsmaGiT7jaVtQka2qR7vJa12zGe3gcwKN/n/bRpgoY26W5TWZugoU26x2tZy6kzaozFAcyoEt4EmyZoaJPuNpW1CRrapHu8lrXb0eSHwDsQ/IVsB6wk+Az5PrBFKtzdHxxrmyZoaJPuNpW1CRrapHu8ltXdH6QCjZwCMrOjgG8SXmqyJ7ABa/yYn5UIv8LMThhjmyZoaJPuNpW1CRrapHu8lvUKM3sFVahz2zDaB8EX9jrx84bAL+LnP7DGj3ln+NMIbmfH0qYJGtqku01lbYKGNuker2V9Gh3v4+jlaOQdQGRok9p6hBdkQPCDPaUY7u63Ex6CjJlNEzS0SXebytoEDW3SPV7LGuPWpQpVRotBHcBHCG9aOpkwEr43hv8jwbdIMXw6wZ/5WNo0QUObdLeprE3Q0Cbd47Ws0yl5p0TqaPJD4GcDzwJudPc/dAtvgk0TNLRJd5vK2gQNbdI9XstalcYOADnMbKq7P9xreBNsmqChTbrbVNYmaGiT7vFa1hxNfgaQY37F8CbYNEFDm3S3qaxN0NAm3eO1rEka6Q3UzI7ORL0c2CIRb5nwQdo0QUObdLeprE3Q0Cbd47WsBkxNhGdp5ABAeBH3lwgbHDrZD1jFmiflnWwAbDaGNk3Q0CbdbSprEzS0Sfd4LStUndWp8sR4UAfhpQbPz4TfnbF5fCxtmqChTbrbVNYmaGiT7vFa1hh3Ryo8d/R84iAPwkukp2fCd8nYvGgsbZqgoU2621TWJmhok+7xWtYYt2UqPHeMu1VAQggh+kMjnwGY2SbAp4ADCZsbAO4FLoyf9y2EnwucCBw5hjZN0NAm3W0qaxM0tEn3eC3rucAcd3+AXqlyuzCoA7gIOAbYqiNsK+Bmgte7YvgxwF/G2KYJGtqku01lbYKGNuker2U9Bvh5le/aRk4BmdlCd985FQ6QiXvC3aeMlU0TNLRJd5vK2gQNbdI9Xss6ZJeyydHUjWB/MrNPmtmWQwHxswGTiuFmdgywfCxtmqChTbrbVNYmaGiT7vFa1hh3BxVo5DMA4GBgNvBLM3tqDLsHuIBQMcXw84BZwPvH0KYJGtqku01lbYKGNuker2U9D3gbFWjkFJAQQojRp6lTQJjZM81sbzPbqBB+eCZ8nwbYNEFDm3S3qaxN0NAm3eO1rPtQhSpPjAd1AEcRXon2f8BtwAEd4SuK4THujrG0aYKGNuluU1mboKFNusdrWWPctZW+a8f6yz4zANwATI2fZwJzCS+JuQH4fTE8/v/YWNo0QUObdLeprE3Q0Cbd47Ws8f9Kr4Qc8y/7zAAwv/D/VMLGiPuAeYnwrwArxtimCRrapLtNZW2ChjbpHq9l/UqnTS/HmH/ZJ0XBpcBuhbDJwF3AqkT46YCPsU0TNLRJd5vK2gQNbdI9Xst6etGm2zHiL+vROIAZdOxyK4S/MWNz4FjaNEFDm3S3qaxN0NAm3eO1rDHuxanw3KFloEII0VIauwxUCCHE6KIBQAghWooGACFKMFiBsFEAAAKWSURBVLOZZvaOLuc8x8zmxWOZmd0aP19sZtuY2dmD0itEFfQMQLQGM5vs7qn3qJbZvAL4hLu/ocfzTwV+4u760heNR3cAYkJhZu82s+vN7Pdm9j0zO9XMvmJmlwHHm9lGZvYdM7vazK4zswOi3Uwz+7WZXRuPF8Uk5wAvjb/oP2Zm65jZl6L99Wb29130zDSzG+Pn95jZ/5nZj+NdwofM7Oio40oz2zyet4OZXWhm10RNzxzFKhMtpqneQIWojJk9G/gnwlK4v8Qv1K8AzwBe7e6rzOwLwKXu/ndmtinwOzO7mPBGpde4+woz2wn4PsHr4mw67gDM7AjgQXff08zWAy43s5+5+609ytwV2B1YH1gMHOPuu5vZV4F3A18DTgbe7+6LzGwvwhugXjXyGhJibTQAiInEq4Cz3f0vAO6+zMwAfujuq+I5rwX2N7NPxP/XB54G3Al8w8x2A1YRBo0UrwWea2Zvjf9vAuwE9DoAXObuywk+3R8EfhzDb4jpTiW89PuHUTvAej2mLUQlNACIiYQRdkkWeaRwzlvcfeFahmbHEXyqP48wNbqiJI8Pu/tFNTU+3vF5dcf/qwnX4yTgAXffrWb6QvSMngGIicQlwNvMbBrA0Jx6gYuAD1v8eW1mu8fwTYC73H018C5gnRi+HNi4YP8BM1s32j+j6JJ3JLj7Q8CtZnZQTN/M7Hn9Sl+ITjQAiAmDu98EfJ7wpqTfE+b/i3wWWBe4Pj6c/WwMPxE41MyuJEz/DN01XA+sjA+VPwZ8G5gPXBvt/4v+30m/EzgsluEm4IA+py8EoGWgQgjRWnQHIIQQLUUDgBBCtBQNAEII0VI0AAghREvRACCEEC1FA4AQQrQUDQBCCNFSNAAIIURL+f+k/zk5SnrnRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df.loc['2019-01-01':].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createTime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-10-07</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-18</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-17</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-14</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-19</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            title\n",
       "createTime       \n",
       "2012-10-07      4\n",
       "2012-11-18      8\n",
       "2013-02-17      9\n",
       "2012-10-14     10\n",
       "2012-08-19     10"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "392"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(plot_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 392 artists>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAE4FJREFUeJzt3X+spFd93/H3p7ZxqoBqO14s115lDdq0MU27WFvHkiNEQvGvVF2QQDKKYBURbdTYFaip2nUi1TSRJRIFiJCoI1O2mJTiOIGIVbytszFUiD+wvSaLvcvW8a1x48Ur76ZODFUkWptv/phz8eT6/pj7Y37cOe+XNJqZM2fm+T7n3nk+9znPM3NTVUiS+vN3pl2AJGk6DABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSp86fdgGrufTSS2vXrl3TLkOStpXHHnvsL6pqx1r9ZjoAdu3axbFjx6ZdhiRtK0n+9yj9nAKSpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnVozAJLsTPLlJKeSnEzygdb+oSTfTnK8XW4Zes4dSRaSPJnkxqH2m1rbQpKD41klSdIoRvkcwEvAL1fV15O8DngsydH22Meq6reGOye5GrgVeBPw94E/SfJj7eFPAG8HTgOPJjlcVd/cihWRJK3PmgFQVWeAM+32d5OcAq5Y5Sn7gPuq6nvAt5IsANe2xxaq6mmAJPe1vgaAJE3Buo4BJNkFvBl4uDXdnuTxJIeSXNzargCeHXra6da2Uru0absOPjDtEqRtZ+QASPJa4PPAB6vqO8DdwBuBPQz2ED6y2HWZp9cq7UuXcyDJsSTHzp07N2p5kqR1GikAklzAYOP/2ar6AkBVPV9VL1fV94FP8so0z2lg59DTrwSeW6X9b6mqe6pqb1Xt3bFjze8ykiRt0ChnAQX4FHCqqj461H75ULd3Aifa7cPArUkuTHIVsBt4BHgU2J3kqiSvYXCg+PDWrIYkab1GOQvoeuC9wBNJjre2XwHek2QPg2mcZ4BfBKiqk0nuZ3Bw9yXgtqp6GSDJ7cCDwHnAoao6uYXrIklah1HOAvoqy8/fH1nlOXcBdy3TfmS150mSJsdPAktSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6tWYAJNmZ5MtJTiU5meQDrf2SJEeTPNWuL27tSfLxJAtJHk9yzdBr7W/9n0qyf3yrJUlayyh7AC8Bv1xVPw5cB9yW5GrgIPBQVe0GHmr3AW4GdrfLAeBuGAQGcCfwk8C1wJ2LoSFJmrw1A6CqzlTV19vt7wKngCuAfcC9rdu9wDva7X3AZ2rga8BFSS4HbgSOVtULVfWXwFHgpi1dG0nSyNZ1DCDJLuDNwMPAZVV1BgYhAby+dbsCeHboaadb20rtkqQpGDkAkrwW+Dzwwar6zmpdl2mrVdqXLudAkmNJjp07d27U8iRJ6zRSACS5gMHG/7NV9YXW/Hyb2qFdn23tp4GdQ0+/Enhulfa/paruqaq9VbV3x44d61kXSdI6jHIWUIBPAaeq6qNDDx0GFs/k2Q98caj9fe1soOuAF9sU0YPADUkubgd/b2htkqQpOH+EPtcD7wWeSHK8tf0K8GHg/iTvB/4ceHd77AhwC7AA/DXw8wBV9UKSXwcebf1+rape2JK1kCSt25oBUFVfZfn5e4C3LdO/gNtWeK1DwKH1FChJGg8/CSxJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygDQtrfr4APTLkHalgwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHVqzQBIcijJ2SQnhto+lOTbSY63yy1Dj92RZCHJk0luHGq/qbUtJDm49asiSVqPUfYAPg3ctEz7x6pqT7scAUhyNXAr8Kb2nP+Y5Lwk5wGfAG4Grgbe0/pKkqbk/LU6VNVXkuwa8fX2AfdV1feAbyVZAK5tjy1U1dMASe5rfb+57oolSVtiM8cAbk/yeJsiuri1XQE8O9TndGtbqV2SNCUbDYC7gTcCe4AzwEdae5bpW6u0v0qSA0mOJTl27ty5DZYnSVrLhgKgqp6vqper6vvAJ3llmuc0sHOo65XAc6u0L/fa91TV3qrau2PHjo2UJ0kawYYCIMnlQ3ffCSyeIXQYuDXJhUmuAnYDjwCPAruTXJXkNQwOFB/eeNmSpM1a8yBwks8BbwUuTXIauBN4a5I9DKZxngF+EaCqTia5n8HB3ZeA26rq5fY6twMPAucBh6rq5JavjSRpZKOcBfSeZZo/tUr/u4C7lmk/AhxZV3WSpLHxk8CS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygDQ3Nh18IFplyBtKwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdWrNAEhyKMnZJCeG2i5JcjTJU+364taeJB9PspDk8STXDD1nf+v/VJL941kdSdKoRtkD+DRw05K2g8BDVbUbeKjdB7gZ2N0uB4C7YRAYwJ3ATwLXAncuhoYkaTrWDICq+grwwpLmfcC97fa9wDuG2j9TA18DLkpyOXAjcLSqXqiqvwSO8upQkSRN0EaPAVxWVWcA2vXrW/sVwLND/U63tpXaXyXJgSTHkhw7d+7cBsuTJK1lqw8CZ5m2WqX91Y1V91TV3qrau2PHji0tTpL0io0GwPNtaod2fba1nwZ2DvW7EnhulXZJ0pRsNAAOA4tn8uwHvjjU/r52NtB1wIttiuhB4IYkF7eDvze0NknSlJy/VocknwPeClya5DSDs3k+DNyf5P3AnwPvbt2PALcAC8BfAz8PUFUvJPl14NHW79eqaumBZUnSBK0ZAFX1nhUeetsyfQu4bYXXOQQcWld1kqSx8ZPAktQpA0CSOmUAaK7tOvjAtEuQZpYBIEmdMgCkZbjnoB4YAJImwlCdPQaAJHXKAJCkThkAktQpA0CSOmUASNIKhg9cz+NBbANA29Y8viE1O3r4/TIAJKlTBoAkTdE09zQMAEnqlAEgSTNi0nsDBoC2pR4O0Gk27Dr4wA9+37b6927av8cGgKSx24oN3bQ3lvPIAJCkGTCNgDMANDH+BdcPf9bbgwGguePGR+M2L79jBoCkLTMvG8ZeGACS5s40gmg7fm+QASBpLs3qRnhcp5RuhAEgrWAW3qDy5zBOBoDmnhsQrcdGfl/Wes6s/g4aAJpLS99wy70BZ/VNuZpZmj5YahZrmgWzPC4GgLTNzPIGRduLASB1aDuEyKzXOOv1jcIAkLaxedgIrWZc6zepcRtlKnKaDABJUzFrG8Otsp3WywCQNHNW2ohu5i/q7bRhnpRNBUCSZ5I8keR4kmOt7ZIkR5M81a4vbu1J8vEkC0keT3LNVqyANE5uNGbP8PfzL22fhu38O7IVewA/XVV7qmpvu38QeKiqdgMPtfsANwO72+UAcPcWLFuae9t5A7OS1dZpHtd3Vo1jCmgfcG+7fS/wjqH2z9TA14CLklw+huVrG/BN3oft+gGpUW33+jcbAAX8cZLHkhxobZdV1RmAdv361n4F8OzQc0+3Nkkj2O4bm9Vs5ANu8zwek7LZALi+qq5hML1zW5K3rNI3y7TVqzolB5IcS3Ls3LlzmyxP82be/ifrqGa5zvXWNkvrMku1TMOmAqCqnmvXZ4E/BK4Fnl+c2mnXZ1v308DOoadfCTy3zGveU1V7q2rvjh07NlOe5kzvb9b1WulgqV6t13HacAAk+eEkr1u8DdwAnAAOA/tbt/3AF9vtw8D72tlA1wEvLk4VSdvJdt1YTPpDSaO+/lbWMerpo5OoZTvYzB7AZcBXk3wDeAR4oKr+O/Bh4O1JngLe3u4DHAGeBhaATwK/tIllSzNjPRuXcc9xz/tB141Y+o9aZv3TuTC5mjYcAFX1dFX9k3Z5U1Xd1dr/T1W9rap2t+sXWntV1W1V9caq+omqOrZVKyFNwla9Kcf99Qa9HifR+vlJYM20cWzUtuK1xrFRnPZXPY9jj2NSZqWO7cYAkDZoMxud7XKAdmkobYeaNToDQFO1HTYom93QT3qZk3g9zQcDQJIB0SkDQJqiWTgjZVb2UjR5BoA0ZLMbw1n6lkppLQaAZpYbzunYLgeotXkGgCZiljcos1ybNE4GgLqy2a8K2KrnSbPAAJCarfyk7ySDwRDSRhkAmig3VstzXDQNBoAkdcoA0NT51680HQaANCYGm2adASBJnTIANHH+ZSzNBgNAkjplAGimuHcgTY4BoLFb70Z9FkNgFmvS8vxZjc4AkOaAGz1thAEgSZ0yACSpUwaApHWZ1+mmeV2v1RgAWtGk3xA9vgGlaTIAJKlTBoA2bK2/2P2LXpptBsAcW7oBntQ/PPd/ykrbgwEwp7byv1stve/GXZoPBsCcWOuv9OH2lW6v1raRPpJmmwGwzW3Fhng4IFYLklHqMBik7eP8aRegjdvMHL8baknuAcyQ5f6KHm4bviz3vNVecxIMFWl7mXgAJLkpyZNJFpIcnPTyJ2mt+fXV5uLXmqeXpM2a6BRQkvOATwBvB04DjyY5XFXfnGQdm7W4QX7mwz+7bPtaj630epI0SZPeA7gWWKiqp6vq/wH3AfvGtbClUybLHexc7blLX2etfut53I2+pGmbdABcATw7dP90a5uolQJhlDNhNnrapCTNmlTV5BaWvBu4sap+od1/L3BtVf2roT4HgAPt7j8AntzEIi8F/mITzx8na9uYWa1tVusCa9uo7Vzbj1bVjrVeZNKngZ4Gdg7dvxJ4brhDVd0D3LMVC0tyrKr2bsVrbTVr25hZrW1W6wJr26geapv0FNCjwO4kVyV5DXArcHjCNUiSmPAeQFW9lOR24EHgPOBQVZ2cZA2SpIGJfxK4qo4ARya0uC2ZShoTa9uYWa1tVusCa9uoua9togeBJUmzw6+CkKROzWUAzNrXTSR5JskTSY4nOdbaLklyNMlT7friCdVyKMnZJCeG2patJQMfb+P4eJJrplDbh5J8u43d8SS3DD12R6vtySQ3jrm2nUm+nORUkpNJPtDapzp2q9Q19XFL8kNJHknyjVbbf2jtVyV5uI3Z77UTQkhyYbu/0B7fNYXaPp3kW0Pjtqe1T/S90JZ5XpI/TfJH7f7Wj1tVzdWFwcHl/wW8AXgN8A3g6inX9Axw6ZK23wQOttsHgd+YUC1vAa4BTqxVC3AL8N+AANcBD0+htg8B/2aZvle3n+2FwFXtZ37eGGu7HLim3X4d8GethqmO3Sp1TX3c2rq/tt2+AHi4jcX9wK2t/XeAf9lu/xLwO+32rcDvjfHnuVJtnwbetUz/ib4X2jL/NfBfgT9q97d83OZxD2CiXzexCfuAe9vte4F3TGKhVfUV4IURa9kHfKYGvgZclOTyCde2kn3AfVX1var6FrDA4Gc/rtrOVNXX2+3vAqcYfIp9qmO3Sl0rmdi4tXX/v+3uBe1SwM8Af9Dal47Z4lj+AfC2JJlwbSuZ6HshyZXAzwL/qd0PYxi3eQyAmfi6iSUK+OMkj2XwSWeAy6rqDAzexMDrp1bdyrXMylje3na7Dw1NlU2ttraL/WYGfzXOzNgtqQtmYNzaNMZx4CxwlMEex19V1UvLLP8HtbXHXwR+ZFK1VdXiuN3Vxu1jSS5cWtsydY/DbwP/Fvh+u/8jjGHc5jEAlku+aZ/qdH1VXQPcDNyW5C1TrmdUszCWdwNvBPYAZ4CPtPap1JbktcDngQ9W1XdW67pM29jqW6aumRi3qnq5qvYw+NT/tcCPr7L8qdaW5B8BdwD/EPinwCXAv5t0bUn+OXC2qh4bbl5l+RuubR4DYM2vm5i0qnquXZ8F/pDBG+H5xV3Idn12ehWuWMvUx7Kqnm9v1O8Dn+SV6YqJ15bkAgYb2c9W1Rda89THbrm6ZmncWj1/BfwPBvPnFyVZ/AzS8PJ/UFt7/O8x+pTgVtR2U5tSq6r6HvCfmc64XQ/8iyTPMJjC/hkGewRbPm7zGAAz9XUTSX44yesWbwM3ACdaTftbt/3AF6dTIaxSy2Hgfe0MiOuAFxenOyZlyTzrOxmM3WJtt7YzIK4CdgOPjLGOAJ8CTlXVR4cemurYrVTXLIxbkh1JLmq3/y7wzxgco/gy8K7WbemYLY7lu4AvVTuyOaHa/udQmIfBHPvwuE3kvVBVd1TVlVW1i8H260tV9XOMY9zGfSR7GhcGR+z/jMF8469OuZY3MDjr4hvAycV6GMzRPQQ81a4vmVA9n2MwJfD/Gfzl8P6VamGwa/mJNo5PAHunUNvvtmU/3n7RLx/q/6uttieBm8dc208x2K1+HDjeLrdMe+xWqWvq4wb8Y+BPWw0ngH8/9J54hMEB6N8HLmztP9TuL7TH3zCF2r7Uxu0E8F945Uyhib4Xhup8K6+cBbTl4+YngSWpU/M4BSRJGoEBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSp/4GLAZKQtkfMR8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(plot_df)),plot_df['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_period = df.to_period('D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crawlTime</th>\n",
       "      <th>creator</th>\n",
       "      <th>last_resp_date</th>\n",
       "      <th>question_id</th>\n",
       "      <th>resp_no</th>\n",
       "      <th>title</th>\n",
       "      <th>update_time</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-06</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>缓慢投资</td>\n",
       "      <td>2019-12-16 12:41:00</td>\n",
       "      <td>340297</td>\n",
       "      <td>10</td>\n",
       "      <td>为赚66元的期权的小小套利</td>\n",
       "      <td>2019-12-18 20:57:19.897</td>\n",
       "      <td>https://www.jisilu.cn/question/340297&amp;sort_key...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-06</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>HENINHAO</td>\n",
       "      <td>2019-12-07 09:41:00</td>\n",
       "      <td>340299</td>\n",
       "      <td>0</td>\n",
       "      <td>谈妥了?降进口罐睡</td>\n",
       "      <td>2019-12-18 20:57:16.024</td>\n",
       "      <td>https://www.jisilu.cn/question/340299&amp;sort_key...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-06</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>88924835</td>\n",
       "      <td>2019-12-06 23:18:00</td>\n",
       "      <td>340302</td>\n",
       "      <td>2</td>\n",
       "      <td>不懂就问：如何开三个封闭基金帐号？</td>\n",
       "      <td>2019-12-18 20:57:16.198</td>\n",
       "      <td>https://www.jisilu.cn/question/340302&amp;sort_key...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-06</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>blsbls</td>\n",
       "      <td>2019-12-08 23:52:00</td>\n",
       "      <td>340303</td>\n",
       "      <td>8</td>\n",
       "      <td>WSJ: 中方表示，贸易谈判仍在正常轨道上。</td>\n",
       "      <td>2019-12-18 20:57:16.602</td>\n",
       "      <td>https://www.jisilu.cn/question/340303&amp;sort_key...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-06</th>\n",
       "      <td>2019-12-06 17:18:15</td>\n",
       "      <td>甜橙飘飘</td>\n",
       "      <td>2019-12-06 21:52:00</td>\n",
       "      <td>340304</td>\n",
       "      <td>1</td>\n",
       "      <td>今天上市的 132021 中国软件 可交换债 19中电EB，感觉是个坑货！</td>\n",
       "      <td>2019-12-18 20:57:16.393</td>\n",
       "      <td>https://www.jisilu.cn/question/340304&amp;sort_key...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     crawlTime   creator      last_resp_date question_id  \\\n",
       "createTime                                                                 \n",
       "2019-12-06 2019-12-06 17:18:15      缓慢投资 2019-12-16 12:41:00      340297   \n",
       "2019-12-06 2019-12-06 17:18:15  HENINHAO 2019-12-07 09:41:00      340299   \n",
       "2019-12-06 2019-12-06 17:18:15  88924835 2019-12-06 23:18:00      340302   \n",
       "2019-12-06 2019-12-06 17:18:15    blsbls 2019-12-08 23:52:00      340303   \n",
       "2019-12-06 2019-12-06 17:18:15      甜橙飘飘 2019-12-06 21:52:00      340304   \n",
       "\n",
       "            resp_no                                  title  \\\n",
       "createTime                                                   \n",
       "2019-12-06       10                          为赚66元的期权的小小套利   \n",
       "2019-12-06        0                              谈妥了?降进口罐睡   \n",
       "2019-12-06        2                      不懂就问：如何开三个封闭基金帐号？   \n",
       "2019-12-06        8                 WSJ: 中方表示，贸易谈判仍在正常轨道上。   \n",
       "2019-12-06        1  今天上市的 132021 中国软件 可交换债 19中电EB，感觉是个坑货！   \n",
       "\n",
       "                       update_time  \\\n",
       "createTime                           \n",
       "2019-12-06 2019-12-18 20:57:19.897   \n",
       "2019-12-06 2019-12-18 20:57:16.024   \n",
       "2019-12-06 2019-12-18 20:57:16.198   \n",
       "2019-12-06 2019-12-18 20:57:16.602   \n",
       "2019-12-06 2019-12-18 20:57:16.393   \n",
       "\n",
       "                                                          url  \n",
       "createTime                                                     \n",
       "2019-12-06  https://www.jisilu.cn/question/340297&sort_key...  \n",
       "2019-12-06  https://www.jisilu.cn/question/340299&sort_key...  \n",
       "2019-12-06  https://www.jisilu.cn/question/340302&sort_key...  \n",
       "2019-12-06  https://www.jisilu.cn/question/340303&sort_key...  \n",
       "2019-12-06  https://www.jisilu.cn/question/340304&sort_key...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_period.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_period.loc['2019-12-18'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>resp_no</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createTime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-06</th>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07</th>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08</th>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09</th>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10</th>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11</th>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12</th>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01</th>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02</th>\n",
       "      <td>446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03</th>\n",
       "      <td>1718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04</th>\n",
       "      <td>1790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05</th>\n",
       "      <td>1369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06</th>\n",
       "      <td>2049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07</th>\n",
       "      <td>2249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08</th>\n",
       "      <td>2458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09</th>\n",
       "      <td>2779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10</th>\n",
       "      <td>2222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11</th>\n",
       "      <td>3649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12</th>\n",
       "      <td>4709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01</th>\n",
       "      <td>4655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02</th>\n",
       "      <td>4740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03</th>\n",
       "      <td>4383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04</th>\n",
       "      <td>5140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05</th>\n",
       "      <td>5374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06</th>\n",
       "      <td>5433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07</th>\n",
       "      <td>5392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08</th>\n",
       "      <td>6066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09</th>\n",
       "      <td>6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10</th>\n",
       "      <td>5524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11</th>\n",
       "      <td>8704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10</th>\n",
       "      <td>24275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11</th>\n",
       "      <td>33277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12</th>\n",
       "      <td>36424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01</th>\n",
       "      <td>38346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02</th>\n",
       "      <td>23993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03</th>\n",
       "      <td>29076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04</th>\n",
       "      <td>25449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05</th>\n",
       "      <td>40045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06</th>\n",
       "      <td>32719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07</th>\n",
       "      <td>24184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08</th>\n",
       "      <td>26086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09</th>\n",
       "      <td>23856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10</th>\n",
       "      <td>20275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11</th>\n",
       "      <td>30454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12</th>\n",
       "      <td>40621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01</th>\n",
       "      <td>38627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02</th>\n",
       "      <td>26721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03</th>\n",
       "      <td>35184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04</th>\n",
       "      <td>31431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05</th>\n",
       "      <td>38814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06</th>\n",
       "      <td>35419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07</th>\n",
       "      <td>34023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08</th>\n",
       "      <td>33649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09</th>\n",
       "      <td>29757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10</th>\n",
       "      <td>32018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11</th>\n",
       "      <td>28166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12</th>\n",
       "      <td>33813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01</th>\n",
       "      <td>34576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02</th>\n",
       "      <td>28948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03</th>\n",
       "      <td>23768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            resp_no\n",
       "createTime         \n",
       "2012-06         543\n",
       "2012-07         320\n",
       "2012-08         416\n",
       "2012-09         359\n",
       "2012-10         363\n",
       "2012-11         441\n",
       "2012-12         371\n",
       "2013-01        1703\n",
       "2013-02         446\n",
       "2013-03        1718\n",
       "2013-04        1790\n",
       "2013-05        1369\n",
       "2013-06        2049\n",
       "2013-07        2249\n",
       "2013-08        2458\n",
       "2013-09        2779\n",
       "2013-10        2222\n",
       "2013-11        3649\n",
       "2013-12        4709\n",
       "2014-01        4655\n",
       "2014-02        4740\n",
       "2014-03        4383\n",
       "2014-04        5140\n",
       "2014-05        5374\n",
       "2014-06        5433\n",
       "2014-07        5392\n",
       "2014-08        6066\n",
       "2014-09        6700\n",
       "2014-10        5524\n",
       "2014-11        8704\n",
       "...             ...\n",
       "2016-10       24275\n",
       "2016-11       33277\n",
       "2016-12       36424\n",
       "2017-01       38346\n",
       "2017-02       23993\n",
       "2017-03       29076\n",
       "2017-04       25449\n",
       "2017-05       40045\n",
       "2017-06       32719\n",
       "2017-07       24184\n",
       "2017-08       26086\n",
       "2017-09       23856\n",
       "2017-10       20275\n",
       "2017-11       30454\n",
       "2017-12       40621\n",
       "2018-01       38627\n",
       "2018-02       26721\n",
       "2018-03       35184\n",
       "2018-04       31431\n",
       "2018-05       38814\n",
       "2018-06       35419\n",
       "2018-07       34023\n",
       "2018-08       33649\n",
       "2018-09       29757\n",
       "2018-10       32018\n",
       "2018-11       28166\n",
       "2018-12       33813\n",
       "2019-01       34576\n",
       "2019-02       28948\n",
       "2019-03       23768\n",
       "\n",
       "[82 rows x 1 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample('M').sum().to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month-1: 137\n",
      "month-2: 96\n",
      "month-3: 143\n",
      "month-4: 155\n",
      "month-5: 163\n",
      "month-6: 179\n",
      "month-7: 220\n",
      "month-8: 213\n",
      "month-9: 241\n",
      "month-10: 241\n",
      "month-11: 290\n",
      "month-12: 397\n",
      "month-1: 446\n",
      "month-2: 333\n",
      "month-3: 421\n",
      "month-4: 368\n",
      "month-5: 402\n",
      "month-6: 455\n",
      "month-7: 464\n",
      "month-8: 481\n",
      "month-9: 428\n",
      "month-10: 448\n",
      "month-11: 657\n",
      "month-12: 1951\n",
      "month-1: 1757\n",
      "month-2: 842\n",
      "month-3: 1305\n",
      "month-4: 2194\n",
      "month-5: 2543\n",
      "month-6: 3375\n",
      "month-7: 6815\n",
      "month-8: 3882\n",
      "month-9: 2633\n",
      "month-10: 1722\n",
      "month-11: 1881\n",
      "month-12: 1741\n",
      "month-1: 2792\n",
      "month-2: 1695\n",
      "month-3: 2108\n",
      "month-4: 1696\n",
      "month-5: 1562\n",
      "month-6: 1677\n",
      "month-7: 2116\n",
      "month-8: 1901\n",
      "month-9: 1880\n",
      "month-10: 1639\n",
      "month-11: 2125\n",
      "month-12: 2582\n",
      "month-1: 2171\n",
      "month-2: 1734\n",
      "month-3: 2171\n",
      "month-4: 1795\n",
      "month-5: 2838\n",
      "month-6: 1892\n",
      "month-7: 1667\n",
      "month-8: 1784\n",
      "month-9: 1724\n",
      "month-10: 1425\n",
      "month-11: 2375\n",
      "month-12: 2806\n",
      "month-1: 2773\n",
      "month-2: 2189\n",
      "month-3: 2617\n",
      "month-4: 2424\n",
      "month-5: 2721\n",
      "month-6: 2768\n",
      "month-7: 2672\n",
      "month-8: 2227\n",
      "month-9: 2048\n",
      "month-10: 2434\n",
      "month-11: 2173\n",
      "month-12: 2435\n",
      "month-1: 2335\n",
      "month-2: 2215\n",
      "month-3: 3878\n",
      "month-4: 134\n",
      "month-5: 0\n",
      "month-6: 0\n",
      "month-7: 0\n",
      "month-8: 0\n",
      "month-9: 0\n",
      "month-10: 0\n",
      "month-11: 0\n",
      "month-12: 0\n"
     ]
    }
   ],
   "source": [
    "post_number_list=[]\n",
    "for year in range(2013,2020):\n",
    "    for i in range(1,13):\n",
    "        print('month-{}:'.format(i),end=' ')\n",
    "        print(len(df['{}-{}'.format(year,i)]))\n",
    "        post_number_list.append(len(df['{}-{}'.format(year,i)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['count']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyzis_data = df.resample('W').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "createTime\n",
       "2012-06-24      27\n",
       "2012-07-01      22\n",
       "2012-07-08      21\n",
       "2012-07-15      22\n",
       "2012-07-22      14\n",
       "2012-07-29      24\n",
       "2012-08-05      26\n",
       "2012-08-12      17\n",
       "2012-08-19      10\n",
       "2012-08-26      19\n",
       "2012-09-02      16\n",
       "2012-09-09      15\n",
       "2012-09-16      15\n",
       "2012-09-23      17\n",
       "2012-09-30      23\n",
       "2012-10-07       4\n",
       "2012-10-14      10\n",
       "2012-10-21      13\n",
       "2012-10-28      19\n",
       "2012-11-04      14\n",
       "2012-11-11      16\n",
       "2012-11-18       8\n",
       "2012-11-25      20\n",
       "2012-12-02      22\n",
       "2012-12-09      23\n",
       "2012-12-16      18\n",
       "2012-12-23      21\n",
       "2012-12-30      22\n",
       "2013-01-06      13\n",
       "2013-01-13      28\n",
       "              ... \n",
       "2018-09-16     581\n",
       "2018-09-23     497\n",
       "2018-09-30     446\n",
       "2018-10-07     224\n",
       "2018-10-14     690\n",
       "2018-10-21     699\n",
       "2018-10-28     533\n",
       "2018-11-04     534\n",
       "2018-11-11     484\n",
       "2018-11-18     500\n",
       "2018-11-25     581\n",
       "2018-12-02     438\n",
       "2018-12-09     487\n",
       "2018-12-16     484\n",
       "2018-12-23     621\n",
       "2018-12-30     717\n",
       "2019-01-06     477\n",
       "2019-01-13     524\n",
       "2019-01-20     486\n",
       "2019-01-27     558\n",
       "2019-02-03     469\n",
       "2019-02-10     188\n",
       "2019-02-17     517\n",
       "2019-02-24     675\n",
       "2019-03-03    1034\n",
       "2019-03-10    1077\n",
       "2019-03-17     834\n",
       "2019-03-24     858\n",
       "2019-03-31     781\n",
       "2019-04-07     134\n",
       "Name: count, Length: 355, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyzis_data['count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(analyzis_data['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b1a54d3be0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IAGhEVhTR626ZOBoFR5mOIwCA1hAcAQCNGORl9DtbOo6qVTUTRwAArSE4AgAakeXbJ46qtbVcxxEAQGsIjgCARgx2nKpWlWMLjgAA2kNwBAA0IttxqlpXOTYAQOsIjgCARuw8Vc3EEQBA+wiOAIBGDFfVtnQcjSaOiuJevSIAAA5LcAQANGK4qrblVLXR9zPJEQBAawiOAIBG7FpVqyaOdBwBALSG4AgAaMSgKKK/ZVWtO+44ulevCACAwxIcAQCNyPJy3GsUYVUNAKCNBEcAQCMGebGtHLurHBsAoHUERwBAI7KijH5368TR8NtcxxEAQGsIjgCARmR5Eb3O1omj4feLQnAEANAWgiMAoBGDHaeqVeXYmeAIAKA1BEcAQCOyooj+lomj6ru54AgAoDUERwBAI7KdE0dVObaOIwCA1hAcAQCNGORF9LecqtYbBUcmjgAA2kNwBAA0IivKcVgUEdFJgiMAgLYRHAEAjRiuqm09VU1wBADQNoIjAKARg6KIfnfCxJGOIwCA1hAcAQC1y4syyjKit+VUtaoo28QRAEB7CI4AgNoN8iIiYvupajqOAABaR3AEANQuG4VD21bVRh1HhVU1AIDWEBwBALXLqomjratqyrEBAFpHcAQA1G6Q7z1xJDgCAGgPwREAULusqDqONm81dBwBALSP4AgAqF02mjiq1tMiIrrVxJGOIwCA1hAcAQC1q05V62+dOKqCo1xwBADQFoIjAKB21alqvS0dR+NVNRNHAACtITgCAGo3mHCqWlWOXeg4AgBoDcERAFC7bMKpahHDziMTRwAA7SE4AgBqt7mqtv1Wo9NJ4x8DAODkExwBALXLi92nqkUMe46sqgEAtIfgCACoXTbqOOruDI46KUY/BABACwiOAIDaZXtNHHVSFDqOAABaQ3AEANQu36PjqNtJkRVGjgAA2kJwBADUbq+Jo06yqgYA0CaCIwCgdnt3HIVybACAFhEcAQC122viqNfpjH8MAICTT3AEANRur46jTieUYwMAtIjgCACo3Z6nqqU0DpUAADj5BEcAQO327jhKkZs4AgBoDcERAFC78cRRd0JwlAuOAADaQnAEANRu3HHU2dFxlEwcAQC0ieAIAKhdNXE0aVWt0HEEANAagiMAoHZVx9HOcuyejiMAgFYRHAEAtdur46jTcaoaAECbCI4AgNrt1XHUTYIjAIA2ERwBALWrVtV2bKqZOAIAaBnBEQBQu6woo9dJkdKEjiPBEQBAawiOAIDa5UW5q98oYniqmnJsAID2EBwBALUbThztvs3opBSFiSMAgNYQHAEAtcvyIro7C45itKpm4ggAoDUERwBA7aqOo506nRRZLjgCAGgLwREAULs9O45SisLEEQBAawiOAIDa7dVx1HWqGgBAqwiOAIDa7dVxJDgCAGgXwREAULu9Oo66yrEBAFpFcAQA1G6vjqNOSlEU9+AFAQBwJIIjAKB2WVFGd0LHUc+qGgBAqwiOAIDaZXkxcVWt00mRCY4AAFpDcAQA1C7bY1Wt24kodBwBALSG4AgAqF2+Vzl2qndVbXk9iyxXmgQA0BTBEQBQuywvozvxVLVOFDUGR3/6H/+n+OUPfKm2zwcAwHaCIwCgdllRRG9COXa3E7V1HJVlGS/fXIuXbqzW8vkAANhNcAQA1C7fo+Oo00mR19RxVAVQG5lVNQCApgiOAIDaZft0HNW1qpblo+BIxxEAQGMERwBA7fbqOOp1Um2raoNiGBitmzgCAGjMgcFRSuldKaXLKaVPbrn2d1JKL6aUPjb653u2/NjfSik9nVL6bErpu7dcf9vo2tMppR+v/6cCAJwUe3UcdUZhUh1TR+OJI8ERAEBjppk4+sWIeNuE6/+kLMs3j/55T0RESulrIuL7IuJrRx/zMymlbkqpGxE/HRF/NiK+JiK+f/RYAGAG7dVx1E3Da3X0HGWjFbWBVTUAgMYcGByVZfmfI+LalJ/veyPi18qyXC/L8tmIeDoivmX0z9NlWT5TluVGRPza6LEAwAzKismrat1RmJTXMHE0UI4NANC443Qc/WhK6ROjVbYLo2uviYjntzzmhdG1va4DADMoy/cux46oJziqJo6UYwMANOeowdHPRsQbIuLNEfFyRPyj0fXdd4gR5T7Xd0kpvSOl9GRK6ckrV64c8eUBAPfScOJo921GNYVUx6raQMcRAEDjjhQclWV5qSzLvCzLIiJ+PoaraBHDSaLHtjz00Yh4aZ/rkz73z5Vl+ZayLN9y8eLFo7w8AOAey4si+hM6jjqpvnLsqttIcAQA0JwjBUcppVdt+de/EBHViWvvjojvSynNp5ReFxFPRMSHIuLDEfFESul1KaW5GBZov/voLxsAOMn26jiqCrOzOk9Vy4//uQAAmKx30ANSSr8aEd8ZEQ+llF6IiJ+IiO9MKb05hutmX4yIH46IKMvyUymlX4+IT0dEFhE/UpZlPvo8PxoR742IbkS8qyzLT9X+swEAToS9Oo5qnTgqqomj/NifCwCAyQ4Mjsqy/P4Jl9+5z+N/MiJ+csL190TEew716gCAVsqLMnrdZjuONieOrKoBADTlOKeqAQBMlBXF3TtVTccRAEBjBEcAQK2KooyijIkdR+OJo1pW1ZyqBgDQNMERAFCrqvh64sRRjcFRNXE0UI4NANAYwREAUKsqFJrUcdQZBUdFDR1HVWBk4ggAoDmCIwCgVtnotLNJE0e98cRRfc+zkRdR1hBEAQCwm+AIAKhVddrZpI6jzqgcuwp96nieCCerAQA0RXAEANRqmo6jGnKjbWGRdTUAgGYIjgCAWu3XcVRdymtYLds2cSQ4AgBohOAIAKhVtYY2aVWt2xneeuR1rKpt+RxOVgMAaIbgCACoVTUJNHFVLdVXjj0wcQQA0DjBEQBQq6rjaGI5drWqVtSxqral4yjPj/35AADYTXAEANSqCoX6EzqOeqPkqKij42hL+LRu4ggAoBGCIwCgVvt3HFWPOX5wNHCqGgBA4wRHAECt9us46ow6jopaVtU2P4dybACAZgiOAIBa7ddxVF2ro+NoUJg4AgBomuAIAKjVfh1H4+Cojo6jraeqKccGAGiE4AgAqNX+HUf1TRxlOo4AABonOAIAarVfx1E31RccbeROVQMAaJrgCACoVRUK9SasqnVGYVJRy6raZlikHBsAoBmCIwCgVlU59qSJo+paVkPQkxVlzI3CKatqAADNEBwBALWqJoEmdRx1Un3l2IO8iKX5bkREbGTKsQEAmiA4AgBqtd/EURUmFbWUY5dxaq4XEREbuYkjAIAmCI4AgFrt13FUhUl1TBxlRRGnxhNHgiMAgCYIjgCAWu03cVSVY9dxqtogL2OxLzgCAGiS4AgAqNV+HUfdVF9wlBVF9LudmOt2YsOpagAAjRAcAQC1upsTR71uin43mTgCAGiI4AgAqNVUHUe1lGOPJo56ndjInaoGANAEwREAUKtq4mjiqlqN5diDvIxeJw2DIxNHAACNEBwBALWqOo4mrqqNOo6KWlbViuhVE0eCIwCARgiOAIBaTTVxVEPOkxVl9Lsp5rqdGCjHBgBohOAIAKhV1V/Un9BxVGVJeXH85CjLi+h1OjHX68a6iSMAgEYIjgCAWlUTRxMGjiKlFN1Oqq/jqJtirptio44RJgAAdhEcAQC1Gk4CpUhpQnIUEd2UalpVK2Ju3HHkVDUAgCYIjgCAWuXFcBJoL91OiqKGiaOsmjhSjg0A0BjBEQBQq6woo9fZ+xaj20mR1VBmPag6jpRjAwA0RnAEANQqy4uJJ6pVOinqmTiqTlUzcQQA0BjBEQBQq+HE0f6ratXJa8d6nryMXrcT/W5HOTYAQEMERwBArQ7uOOqMT147qrIsY1AU0e+YOAIAaJLgCACo1UEdR/1uisExJ4TyooyyjOh1OzHf68S64AgAoBGCIwCgVgd1HC3OdWN1kB/vOUYTS71uirluJzay430+AAAmExwBALU6qONosd+NtY3jBT3VxFK/04m5nlPVAACaIjgCAGp1UMfR0lw3Vo4ZHGX5lomjnnJsAICmCI4AgFplRRndfTqOFud6sXLMVbVBMQyKqlPV8qKs5aQ2AAC2ExwBALXK8uKAVbXOsVfVqomj6lS1iHCyGgBAAwRHAECthhNH+62q9WJlkB3vOargqNuJua7gCACgKYIjAKBWeVFGf5+Oo8W5bqwetxx7vKqWYr6aONJzBABQO8ERAFCrgyaOFvvHD462TRwJjgAAGiM4AgBqNew42vsWY2muGyuDPMry6GXWg1FI1Ouk6FtVAwBojOAIAKhVXpTRO2BVrSwj1o8R9FTB0baJI8ERAEDtBEcAQK2yojzgVLVuRMSx1tWyYjit1Osm5dgAAA0SHAEAtcryg05VGwZHK4OjB0ebq2o6jgAAmtS71y8AAJgtWbF/x9Hi3PD2Y3UjO/pzjMuxU5Rh4ggAoCmCIwCgVgd1HC2NV9WOHvRkxWjiqNuJNHoqE0cAAPUTHAEAtcqK/VfVFqtVtWNMHA1GE0e9TopOMnEEANAUwREAUKssP6Acu4aOo81VtU6UMfy+4AgAoH6CIwCgVsOJo707jqpy7LVjnao2DIn63RQRw5BqIz/65wMAYDLBEQBQq7woRoHOZIv9alXtOKeqbU4cVR1Hg6w88ucDAGAywREAUKupO46OtapWlWOn8XOtK8cGAKid4AgAqNVBHUdLc8Pbj+Osqg2Kqhy7M55u0nEEAFC/vQsIAACOID+g46iWVbVss+NorudUNQCApgiOAIBaZQd0HHU7w7BnZZAd6zkiInrdTsx1BUcAAE0RHAEAtSmKMooy9u04ihierHasVbW8WlVL0et2opMiBjqOAABqJzgCAGqTFZuBzn4W+91jraplW05Vi4iY63ViQ3AEAFA7wREAUJt8FBzt13EUMTxZ7VinqhVFpLQ52TTX7VhVAwBogOAIAKhN1T20X8dRxHBVbfWYq2r9LeHUXK8T64IjAIDaCY4AgNpUK2QHdRwt9o8XHGV5Eb0t4ZSJIwCAZgiOAIDaTN1xNNc75qpaOe43ihhOHCnHBgCon+AIAKhN1XHU6+5/i7HU78bqRnbk5xnkxbZ1uLmeiSMAgCYIjgCA2lQdRweuqs11Y/U4E0d5Gb0dHUdOVQMAqJ/gCACoTdVxdPCq2nHLsbd3HPV1HAEANEJwBADUpuo4OmjiaKnfjZXjBEc7O44ERwAAjRAcAQC1qTqO+gd1HI1W1cqyPNLzZHmxbarJqhoAQDMERwBAbabtOFqY60ZZRqwfcUpokJfbCrjnlWMDADRCcAQA1GbajqOlfjci4sjralkx4VQ1E0cAALUTHAEAtZm642iuFxFx5JPVhqeqbQmOdBwBADRCcAQA1GbajqOFueHE0epGdqTnGZ6qtvkcTlUDAGiG4AgAqM20HUfHX1UrraoBAETEP3zvZ+M/fvZyY59fcAQA1GbqjqPxxNFRV9WK6HU2b2Pmep0YmDgCAO4zT1++E//sd5+Of/Pxlxt7DsERAFCbfMqOo2pVbeWIHUeDvNy2DjfX68S6iSMA4D7zG0+9EBERN1cHjT2H4AgAqE02ZcfRsSeOdpyqNj/qOCrL8kifDwCgbfKijH/90WFwdEtwBAC0QZZP23E0OlXtiMHRIC93lWNX1wEA7gf/5fNX4tKt9Tg11zVxBAC0QzVxdFDH0cLc8Bbk6KtqRfQ728uxI0JBNgBw3/iXT70QF5b68We+5pFGg6NeY58ZALjvVB1HvQNX1aqJo+xIz5PlZfS6u4OjQVZEzB/pUwIAtMbNlUG871OX4i9962uj10kmjgCAdph24mixX3UcHW1CKCuKbeGUiSMA4H7y7k+8FBt5Ef/jNz0a5xb7sTrIYz072iT3QQRHAEBtpu046nZSzPU6sTI42sTRIC+3r6qNQqSNTHAEAMy+33jy+fgjX3EmvvbVZ+PcUj8imjtZTXAEANRm2omjiOHJakc+VS2fPHG0LjgCAGbczdVBfPyFm/HfvelVkVKKc4vD4Kipk9UERwBAbabtOIqIWOofPTgaFDs6jkwcAQD3iTvrw4nti2eGxY5nF00cAQAtUU0cHbSqFhGxMNc98qlqWV5Ev7N74mig4wgAmHEro+CoOmzknOC41s4LAAAgAElEQVQIAGiLquOoyVW1oiijKGPiqWrKsQGAWbcyun86NT88bOSeB0cppXellC6nlD655doDKaX3pZQ+P/r2wuh6Sin9VErp6ZTSJ1JK37jlY94+evznU0pvb+RnAwDcU4eZOFrq944UHA2KYTjU39pxZFUNALhPLG8MJ44W+zsmjlbu3cTRL0bE23Zc+/GI+O2yLJ+IiN8e/XtExJ+NiCdG/7wjIn42Yhg0RcRPRMS3RsS3RMRPVGETADA7qo6j/hQdR4tHXFUb5NVzTJg4EhwBADNudc+Jo6OdVnuQA+/qyrL8zxFxbcfl742IXxp9/5ci4s9vuf7Py6EPRMT5lNKrIuK7I+J9ZVleK8vyekS8L3aHUQBAy1UTR1MMHMVivxurG4e/wdlch9u8jamCKqeqAQCzbnkUHC3NDYOjfrcTS3PdE9dx9EhZli9HRIy+fXh0/TUR8fyWx70wurbX9V1SSu9IKT2ZUnryypUrR3x5AMC9kOVF9DopUpqy46imiaN5HUcAwH2ieuOtKseOGE4dnbTgaC+T7hLLfa7vvliWP1eW5VvKsnzLxYsXa31xAMD0Pvrc9bh6Z/1QH5MX5VT9RhHDVbWjdBxlo46jXnfCqWomjgCAGbe8vn3iKOJkBkeXRitoMfr28uj6CxHx2JbHPRoRL+1zHQA4oX7wnR+Kn/8vzx7qY7KinKrfKGK4qrZylOBoNHG09eQ2p6oBAPeLamJ768TR2cV+3DphwdG7I6I6Ge3tEfFbW67/4Oh0tbdGxM3RKtt7I+K7UkoXRqXY3zW6BgCcQEVRxu31LF455MRRlhdTTxxVq2plOXEIeU+D3KlqAMD9a3k9i14njd84i4g43+DEUe+gB6SUfjUivjMiHkopvRDD09H+fkT8ekrphyLiuYj4i6OHvycivicino6IlYj4qxERZVleSyn9vYj48Ohxf7csy52F2wDACVFN7hz2BiQrym2TQPtZnOtFWQ4LrRf63YM/YMtzRET0nKoGANyHVjbybWtqEc2uqh0YHJVl+f17/NCfmvDYMiJ+ZI/P866IeNehXh0AcE+sjUagb64c7gbkUB1H/WHYs7KRHyo4GuxzqppVNQBg1q1sZNvW1CJOZscRADDDqmPtjzJxNG3HUXXDc9iT1bIJp6pZVQMA7hfLG3ksze+eOFod5I3cCwmOAIBdqomjG6sbh/q4w3QcLY5GrKsjZad+jgmnqnU6KfrdZOIIAJh5q5NW1Zb6EXH4N/2mITgCAHY5zsTR1B1Ho/W0w56sNpgwcRQxnDoycQQAzLrl9cmrahGCIwDgLlkfDAOYtUExnj6aRl6U20qr97M0njgafv5//8mX4yf/3acP/LhJp6pFDAuyBUcAwKxbHeRxasfE0VnBEQBwN61lm2HRrUPcgGRFGd3OdLcX1arayiiYeuf7n41f/dDzBz/HaOJo52RT38QRAHAf2G/i6DD3bdMSHAEAu1QTRxGHe+cqy4vpV9W2TBzdWc/io8/diDvrWWQH9BTtN3E00HEEAMy4iR1HJo4AgLtp63rajUNPHE25qtYfnaq2kceHnr0aWTGcJLqzvn9ZdvW4nStxc71OrAuOAIAZtyw4AgDutfUtK183V6a/AcmLcldp9V62rqq9//NXx9dvre4fHFVTRb0dK3GL/e64LwkAYFatbuSxNK8cGwC4h+7KxNF4VS2L33v6lXHgdGtt/+fL9jhV7fR878BpJQDYaXUjj0++ePNevwyYyiAvYiMvYqm/feKo3+3E0lxXcAQA3B3bJo4O3XE03e3FwuiG57lrK/HZS7fjO564GBEHlzpmxWjiaEfH0ZmFXtxZExwBcDi/8sEvxV/4md8ztUorrIx+ne6cOIoYTh0JjgCAu2LrxNHNlY2pPy4/xMRRt5NivteJ3/7M5YiIeNvXfcXw+Q644RlUE0cdE0cAHN8L11djkJexOhAccfKtbAzvdXZ2HEUIjgCAu6iaOJrrdQ43cXSIjqOI4U3PyzfX4vxSP/7Y6x+MiGlW1SZPHJ1eEBwBcHiXbq1FRMRG5oAFTr7xxNGE4Ois4AgAuFvWs+FNycNn5g/XcZRPP3EUMSy0joj4tjc8GOeXhqWOB5Vj73Wq2un5vlU1AA5NcESbrKxXwdHkVbWDVv6PQnAEAOyyNihirtuJB07NHXLiaPqOo4jNk9W+/Y0Pxam5XnTSwRNHG6OJo35nd8fRRl6MQ69ZM8iL+J9/8cPx8edv3OuXAveFF2+sxj947x9GMQqrmV2Xbq1HRMRGPpt/fzBblkeraqesqgEA99J6lsd8rxPnFvtxY2X6G5DDdBxFbL5b9t++8aHodFKcWTj4nbL9TlWLiJmdOrp6ZyN+5w8vx+8/c/VevxS4L/yHT1+Kn/7dL8Tz11fu9UuhQWVZxuXbw4mjtYGJI06+qsR9UXAEANxLa4Mi5vvdQ488Z0W5a4VsP0tz3Xj0wmK89oGliIg4u9iLWwcEP1XH0c6AahwczWjPUVVY3sQIOrBbVZR8/RDhOe1zfWUwPnShmmiFk2w8cbTHqWorG3kMav61vPuZAID73taJo0OtquVl9A4xcfTX/vQTkRdlpDT8mLNTTBwNRgXc1cdUTi8Mb2tuz+jE0dpoBa+JdxKB3aqw9vry9CdL0j5fvrk2/r6OI9qgKseueiK3Orc47Iu8uTqIh07P1/acgiMAYJf1QREL/U6cX+rHjdVBlGW5K6iZJCvK6B6i4+jb3vDQtn8/u9Cf6lS1ST1KZ2Z+4mj4BY3gCO6OauLomuBopl26LTiiXVbW9584iqg/OLKqBgDsMpw4Gq6q5UUZyxvTFYbmRbGre+gwzi72DjxVbZBPXoerJo5mteNovKo2oz8/OGnWNqpVNcHRLLt8S3BEu6wMqlPV9p84qpPgCADYZW00cVTdgNyY8gun7JDl2DtNNXFUFNHv7r6FmfWOo/XMxBHcTZsdR4KjWVadqBah44h2WFnPo5Mi5nu774XOCo4AgLtlc+JoLiKmvwE5bMfRTmenKOPe6znGHUczGhxVE0e3BUdwV1TrodeW/Z6bZZdMHNEyKxt5nJrrTawQqN7wq+6lyrKMvCiP/ZyCIwBgl/Vs+8TRzSlPFcoP2XG007nFfixv5OOT0yYZ5OXEiaMz88PXOuuraiaO4O5YVY59X7h0az3Ojt54EBzRBisbWSxOWFOL2L2q9jd/4xPx3//T9x/7OQVHAMAua4PhxNH5pcONPGfH7Tia4mS0rCgmdhwt9DvR7aS4sz6bwcr6aPrh1tqwrBxoVhXWXrOqNtMu316Lxx5YiojhtC2cdCsb+cRi7IjY9obfv//ky/Evn3ohPv3yrXjlzvrEx09LcAQA7LKeFTG/teNoiuCoKMooyjhex1E1Yr1Pz9Feq2oppTg935vdiaPRFzSDvBxPQgDNWTNxdF+4dGstHrtQBUcmjjj5VjayWOxPnjia63Viaa4bz76yHP/bv/5kPHBqWDnwiRduHOs5BUcAwC5rgzwWDjlxlI126I/VcbRQ7ebvHf5s5JPLsSOGBdmz3nEUYV0N7obNcmy/32ZVXpRx5fZ6PPbAYkQox6YdhhNHk4OjiOHU0W9+9MW4tTaIX3j7W6KTIj72/M1jPafgCADYpZo4Wux3o99NcWOKL5yq8sXjdBxNN3G0d3B0ZmGGJ44Gm1/Q7BesAfVY3dg8Vc166Gy6emc9ijLGq2o6jmiD5Y08Fucmr6pFbK6r/dif/qr4xtdeiK965Ex8/HkTRwBAzdYGeSz0u5FSinOLc1NOHA1vuI/VcbQ4vBHa72S1rCgndhxFDCeO7pg4AmpQhbV5UcatGQ2k73eXbg17X77i7EL0OklwRCusrGdxao9y7IiIN1w8Hd/8+IX44T/++oiI+PpHz8cnXrhxrABccAQAbFOW5XDiqDe8TTi32Ns3yKlkeTVxVMOq2j4TR4O8iP4eU02nF2Y5ONo6cSQ4gqatDfJxj4ieo9l06dZaREQ8cnYh5nodwRGtsLKR73mqWkTEP/3+b4hf/V/eGr3RdPbXP3Y+rq8M4vlrq0d+TsERALDNRl5EWUYsjL5gOr80FzdWD/6iqZaOo8WDO46y/ICJoxmdDFjLTBzB3bQ6yOPV5xciwslqs+rS7c3gaL7X0XFEK6xsZHFqn1W1TieNQ6OIiK9/7FxERHzsGAXZgiMAYJvqVJnNiaP+VEFFHR1Hp+a60Un7ByODotx2Q7TVmYXZLsc+PTp+V3AEzSrLchQcDUuTbwiOZtKlW+uRUsRDp+dirteJ9YHgiJNvZSOPpX0mjnb6qkfOxEK/c6yeI8ERALBNdeM8P5o4OrfY31aOPciLieP8VcfRXtNA00gpxdnF/sHl2HtMNc3yxNH6oIiLZ+YjYv9VPuD4qsnLV58bBkfXlv2em0WXb63FQ6fno9ftDFfVTBxxwuXFsE5gaZ+Jo5363U587avPCY4AgPpUJcx7TRz977/1yfjLv/CBXR9XdRwdZ1UtYthztG859r6rav1YHeSRzeDN/9pg+A7j6fmeiSNo2NrG8M+QauJIx9FsunRrLR45Owzk57o6jjj5VjaGb46dmp9+4ihiWJD9yZduHvn+SHAEAGxTraotbJk4ur2WRV6UkeVF/LtPvByf/fLtXR+XFccvx44Ynqy23wlGg6LYc1Xt9MLwHbjl9Xzij7fZWjY86e7cYn/fDijg+FZHAfpDZ+ai10k6jmbUpVvr8ciZYY/VXK87/vsPTqqVjeGfTfuVY0/y9Y+di7VBEZ+7dOdIzys4AgC22TlxdH6pKqwexEefvxG31rK4tZZtOx4+YrPjqL9HqDOtaSaO9lpVOzPqALq9PnsTOWuDIhb6nTg7ZecUcHRVcLQ0140Lp+ZMHM2oy7fX4uGzVXBkVY2TrwqO9ivHnuTNj52PiIiPH7EgW3AEAGwzqRw7IuLG6iB+9w8vjx935fb6to+rOo6OPXG0sH/H0SA/eOLozgwWZK8N8ljodePsQk/HETSsCsYXet14YGkurps4mjkbWRGv3NkYr6rNdzuxkc3etCqzZXl0f3PYiaPXPrAU55f6R+45EhwBANusV18wjVbVqomjm6uD+N3PXom5UaB0eXSMcaW2jqPF3r6rWIO8jP6eHUej4GgGC7LXBltX1QRH0KRq4mhhrhsXTvXjunLsmXPlzvDNj0dGE0fzfR1HnHzVn02HnThKKcWbHj0fHxMcAQB12Gvi6LNfvhWfeflWvO1rvyIiIi7f2jlxVFPH0QETR1lR7LkOV00c3Z7JiaMi5q2qwV2xVvWI9LvxwKk5HUcz4v/96IvxKx/8UkQMi7EjYls5to4jTrqjThxFRHz9o+fic5dux/oRJusOF1MBADNvbcfE0bnFuYiI+K2PvRQREf/TNz8W7/74S3F5x6pabR1Hi/1Y2chjkE8OiLK8jF5n8nOcmeGJo/VROfZiv2viCBq2lm0GR+eXdBzNine+/9n4gxdvxtqgiNecH04aPXxmS8eR4IgTbrXqODrkqWoREReW5qIoh6dGzvcO9/GCIwBgm70mjn7/mavx6nML8dbXPxjdTtq9qlZbx9FoamgtiwdOze368WGgtMeq2kx3HBUx3+vE2YV+LO8TrAHHt7qxebpk1XFUFGV0jvnnG/fW1Tvr0euk+Hv/9tPxzY9fiIjNVTXl2LTB8ig4WuofPsrpj+7rjvLr3N0GALBNNcK8OXE0DI7KMuI7/8jD0e2keOj03O5y7No6jjZPcZskK8ro3YcdR9XE0bnFzWANaEbVI7LYH56qVpShlL7lyrKMq8sb8Vfe+pXxnV99MT78xevR66R4cPQGxVzXxBEn38rG8O/+pSNMHM13BUcAQE3WBtsnjuZ6nVga7dL/ya9+OCKGo/17raodd+KoCqomfZFWlmXkxd6ralVZ5Kx1HOVFGYO8HJ6qtrhZVg40Y7McuxMPnBr+nru+4vdcm61s5LGeFfGqcwvxf/2Vb4pve8OD8caHT4+nyKyq0QYr1cTRETqO+r3hr/XBEX6dW1UDALbZOXEUMQxzsryMb3vDgxER8fCZ+Xj55s5Vtfo6jiJi4slqg7x6jsnhVKeT4vR8b+YmjjZ7pzqbwZrgCBqz9XTJC0vDiZRryxvxuodO3cuXxTFcvTPsqXrg1Fws9Lvxf//Qt24rwxYcDb10YzVefX7xXr8M9rCynkVKEQuH7CiKiJjrDj/GxBEAcGw7J44iIl59fjG+/Y0PxqnRKtjDZ+d3TRxleV0dR3tPHFU9Sr19wqnT8724sz5bocrWwvJzJo6gcas7TlWLCAXZLXd1efh31oOnh/8/O5207WSq+V431u/zjqOnvnQtvu3v/0588sWb9/qlsIeVjTwW+90j9a1Vb7odJSA1cQQAbLOe5dHrpG3hzM/+lW+MuS3/fvHMQlxdXo8sL8aPqyaOjt9xNLw9mTRRM5iiR+n0Qm/myrHXsqqot2NVDe6C1cHwz8F+t7M5cbQiOGqza6Pg78FT8xN/vJo4KssyUro/S9B/7+mrERHx7CvL8XWvOXePXw2TLG/ksTR3tBinKscemDgCAI6rOr1rq4fPLMT5pc0Tzi6emY+yjLi65R346h36ud4xV9X2mzga3ezstw53er43c8XRkyaOFPVCc1YHw3f1IyIumDiaCVtX1SaZP8aJU7PiqS9dj4iIV+6sH/BI7pXVjSxOHaEYO2JLOfYRJo4ERwDANutZHvP9/W9KHj4zfMf28q3Nm8tnXlmOfjcduxthaa4b3U7at+Nor1PVIiLOzOLE0Sg4mu91x8GaiSNoztqgGP85eGquG3PdjnLsE+b22iB+/cPPR1mWUz2+eqOjWlXbae4YX1TPgqIo46PPDYOjnaemcnIsb2yG2oe1OXE03e+ZrQRHAMA2a4MiFg6YGhoHR7c3C7KfvnwnHn/w1LHLsVNKcXahN3Giphqv7u9xqlrEcOJoeeaCo81VtYV+J+a6nYnBGlCPtUEei3PDP2dSSnHhVN/E0Qnzbz7+cvzNf/WJ+OLVlakef215PRb73T3XfKpp2fs1OPrClTtxazSta+Lo5FrdyMd9k4c1Dkfz/NAfKzgCALZZz4qDJ47OLkREbCvIfvry7XjikdO1vIazi/2JHUfVzexeqwYRMZOnqm094SmlFGcXeyaOoEGrO97Vv7A0p+PohHnxxjAwuj7l/5erdzb2/btj7j5fVfvIaNro7EIvXrnj1/pJtbyRxdLcESeOxlN1Jo4AgGNaH+S7Oo52unh6+6ra2iCP566txBsv1hQcLfTH73xu9cyV5YiIeP3FvY/EPr3Qi9uzNnGUbQZHEXsHa0A91rLdwZGJo5Pl5RvDiddpQ/Sryxt7rqlFWFV76kvX4/xSP77htResqh3ByzdX413vf7bx51ndyI8cHM31RqeqKccGgL2tbuTjU1XY29oUE0dzvU5cWOqPV9WefWU5ijLiDQ/XNXE0eaLmmVfuRK+T4rEHlvb82DPzw46jaXsv2mDrqlpExLnFvnJsaNDqxvautwdOmTg6aV66uRoRk0/gnOTq8no8OM3E0X0aHH3kuRvxja+9EBfPzFtVO4Jf/eBz8Xf/7acb/283nDg66qra8M+0gXJsANjbP3jvZ+Mv/8IH7/XLOPHWB/mBHUcRw5PWqlW1py/fiYiIJx4+U8trOLsweaLmmSvL8doHlvY/VW2hF2UZsbJx+B3+k2p8qlpvNHG00LeqBg1aG+yYODrVjxvKsU+Ul28ebuLo2p2NeODU/J4/Xk3art+HwdGNlY14+vKd+KavvBAPnZ6Pq3c2ZurNl7vhc5eG90FN/zlxnImj/mjiaGDiCAD29uVbq/Hi9elKNO9n00wcRUQ8fHZ+HBx9/vKdSGn/FbLDGK6q7b75evaV5QOf4/T88NSxWTpZbXPiaPj/5ZxVNWjU6o7g6IGlubixshF54Yvpk6Asy3FwNM0X6mVZxtXljXhov1W1ExIc5UV5qNBmIyvi//i3nz7WetlHn78RETGeONrIi1YewFCWh/tvV6fPXbodEc2feLq8foxVte7Re7wERwDcN5bX85lbYWrCtBNHF8/Mx5Vbwxv3L1y+E699YGkcbBzX2cXerpvWoijj2VeW43UPHRAcLQxHuG/PUEH2eOJotKqmHBuatTYoxr/fIiIunJqLopx+LYrjefHGalzdZ+Xn6vLGeKVsmj8LlzfyWM+K6cqx73Fw9MO//FT8jd/4xNSP/4MXb8QvvP/Z+HefeOnIz/mRL12PbifF1z92bhyuXbmzdsBHnRyrG3n809/+fHzdT7w3fvG/fvGuP//aII8vXh12MN5cbW6ltSjKWB3kR15V6x/j17jgCID7xspGFsWMrTA1YZpT1SKGq2pX7qxHWZbx9OU7tRVjRwwnjlYH+babmxdvrMZ6VsTrD3ieM6Njamdq4mhHOfaw40gICk1ZHeSxOLe9HDsi9BzdJT/wCx+Mv/NvPr3nj1fF2BHTBUfXRqeE7RcczZ+AU9VWN/L4T5+7HJ95+dbUH/PC9WHX06cP8TE7feS56/FHX3UmluZ648Mvrtxux6/1d3/8pfiT//A/xj963+didZDHJ188+n+Ho3rmyrDnMaLZiaPV0ZtIJo4AoEFVYDRLkyhNmL7jaD4GeRlX7qzHM6/ciTfWVIwdEfHI2YWIiHh+y2rhs6+MTlSbcuLozgz9f65W1aovbM4u9CMvylgWgkIj1jbybROUF0aBw1XHlDfu+Wsr8cwry/Hctb1Xy6ti7G4nTbWqdnV5OL300Om9O46q4uB7OXH0keeuxyAvD1Ww/OKN4wVHeVHGx0bF2BERD50Z/jdqQ0H2ldvr8WO/9tF48PRc/PoP/7H4b15zbnxox930+cu3x9+/2WDH0fLG8L5maf6IE0ej4GiQHf5NJ8ERAPeNzeDIqsF+hh1HUwRHZ4c3l099cXijW2dw9A2vPT/83F+6Pr72zJVh8eTrDuw4qiaOZuf/8/ogj/leJ1IaFlueWxz2OFlXg2asZds7jqo/3/7wy3d/muF+8/tfuBoREZdu7h0AvDwKS17/0Kmp1gevTjFxdBJW1T74zPDnfvXORhRT9mm9OJo4+tylO0cqPf7USzdjeSOPb/rKYXBUTRy1ITi6uTqIoox4xx9/fXzL6x6Ii2cWjtX1dFSfu3Q7up00ek3NvWm1OrqPXTpiLUC3k6LbScqxAWA/K6N3am7P0ApTE4YhxXSrahER/3V0k19ncPSGi6fj3GI/nvriluDoleU4M785Rr+XKjiapcmytcH26Yezo+BI3wrUb5AXMcjLbb/nXn1uIS6emY+PPnfjHr6y+8Pvj8KTK3fW9ywjf/nmWsz1OvH4Q6emW1VbPkRwlN+7Sc4PPHMtIiKyopz6jYFqVW0jK+KZK8tTPs/V+OFffjK+4//8nfgf/tnvRUSMg6Nzi/3oddI9CWAOa7P/b/h7deuhHXfT5y7didc9dCpOz/fiRoMdR8vrw5/vqfmj90nOdTtW1QBgPyvrVtWmsZ5tL4Xdy8OjcfbqJr/O4KjTSfFNX3khPvyla+Nrz1wZnqhWTd3s5czCDHYc7SjqNXEEzam+GN06cZRSijc/dj4+9rzgqEllWcZ//cIr0e2kyItyvGK200s31+JV5xbiwlJ/qj8Hr46Cowf3OVWtWgVeH9ybiaO1QR4fe/5GvPrc8E2ZaSd+XryxOj40YppupPf8wcvxA+/8YHzs+RvxpkfPx9/47q+O/+cdb41HLyxFxPDv3wdPz7Vi4mh91P9X/b97+Mx8XNtSnH63fO7S7fiqR4ZveDX593I1MV+dHnsU/W5Sjg0AeynLMlYGVtUOkuVFZEU53cTRaFXt6ct34ivOLsSZhaPfyEzyTV95IZ65sjx+p/jZV5YPLMaOiDg1P3sdR+vZ9omjcyaOoDFVAe3CjgLaNz92Pp59ZTluKMhuzDOvLMelW+vxx594KCIiLt+aHF68fGM1XnVuIc4t9qea8Lh6Zz0W+919T6Oaq6kce3Ujjxeu793PtJePPHc9NvIi/tybXhURw4mrg5RlGS9eX43veOKhmOt1Duw5+rUPPRc/+i8+Em969Hz8fz/2J+Kn/9I3xo/8yTfGt77+wW2Pe+j0fLzSgj6vKuQbTxydOVzoVofVjTyeu7YSTzx8ZnhwRYN/L18aTVNV919HMdczcQQAe1rPivHI+ywFCnVbz6qbsINvEZbmeuO1sDqnjSrf/PgDETHsOVrdyLe9q7qffrcTC/3OzE0czW8pLD+7YOIImlJ9Mbq4o0ek6l4zddScavX5z3/DayIi4tKtyT1HL99ci1edW4xzi/1YGxTjyZO9XFve2HdNLaK+jqOf+p3Px5/7qfdP3VFU+cAz16KTIt72dcPgaJrg5vrKIFYHeXzlg6fiqx85s+/E0b/44HPx47/5B/EdT1yMX/6hb4lzS3u/2XPxzHw7VtV2nDhaTULfzdf+hSt3oiwjvuqRM41PHF0e/X6oDhA5irluJwYmjgBgstUtp09ZVdtbtaIxzcRRxOZNWhPB0ZsePRf9boonv3Rt80S1A4qxK6fn+zPVZbW2x8SR4AjqN5442hGgv+nR85GS4KhJH/jC1Xj1uYXxGwdfnhAc5UUZX761Np44ijj4z8Kryxvx0D5rahGbR5WvHzM4+tCz1+Lm6iAuHfJ0rw88czW+9tXnxieHvjJF+FEVY7/m/GL80VediU+/dCvKcndgVZZl/Mx/fDq++fEL8fM/+JZ9J68iqomjFgRHg+1vdl0c3ZPczZ6jz10anqhWrapNc8rfUV26tRYL/U6cXTjaqWoREf1eRzk2/P/svXd4HNd99X9me2+oi0oSbAB7J9VFUbQUyZabIstNtmUrShy3xPGbvJbt/Owktt8UlzixYzkusixZxZZkdYmkZMlib2AnCJLodRsW28vM74+ZO9gyuzu72AUB8H6eR48kYBe7i13MzD33nPOlUCiUXJARpgCNqjm739EAACAASURBVOWjGMcRMHWRVgnhSKdWYmWjFUd6vFPCUbW8xzHrVPPKWRaJJ6FLEfNMwkWjfx69RgpltkA2GjIdRyatCktrzbQgu0KwLId9l9zY1laNGrMWDAOMSkTVxif50mynTQ+rgReDCsWD3MFoYceRcvqOo3iSxanBCQBAr1t+XI30G21d5BDLqeUIN4M+/jGa7Hp0OC1wB2OSokn3WAAD3jDeu65RdFblo9qkhTsQkxShZhNiObZqqhwbAMaKFO2mQ9doAGolgwXVRthkdm6Vyqg/ijqLrmDXYz5oOTaFQqFQKHlIdRzRxXZuinYcCXbpJRUQjgBgY6sdJwYmxBHYC6oNsu5n0qrmX1QtRcxTKhhYdCp4g7O/g4JCmWtIlWMT1jbb0Dngm/UL6rnI+dFJeIIxbGurglqpQLVJK0ZzUhma4F02DSmOo0IuD08ghqoCEzkVCoYvDp5Gx1HX6KS4AdPrljfhDACO9fkQS7DYuqiqqHLqgTTHkQUAJHuO9pwbAwBsX14r6/lUmzSIJVn4KzhavhxEMjqOqk284JirG6sSXBidxMJqI9RKRcWjaqP+COrMpcfUAD7OH0sUf/yiwhGFQqFQrgqCKcLRfBIU8tHrDuLieKCo+xTrOKpkVA0ANrQ6EEuy+EPnEBqsuoL2eoJJOw8dRxmL2IU1JnSPFff+UiiUwpComlZCOFrXYoMvFEdPEW4SijxIv9G2Nr6ouc6ilew4GvbxXyMdR0D+qBrHcXAHY6gq4DgCBDfGNBxHnf0T4n8X4zjaf8kNBQNsFCJ6csupB7xhGDRK2AxqtDcIwtFQtnC0+9wY2p0WOK16Wc+HuInHAzPn3CmFSEasVK1UwGHQzGhU7fzoJJbUmQEAFr0a0QQrPq9yM+qPoM46TeGIlmNTKBQKhZKbUPTqi6p99ZlT+MrTJ4q6T7GOo/eubcTnti8uuJNbKhta7QD4C3A5E9UIZp0KZ4b9+PGbF+fFBKRogs0SjpbVmcRuBQplprganDZ5HUdiQbZ3Rp/TfGLQF5b8+r6LLiyoMqDRxosbdWadZFRtmDiObPI6joKxJKIJtmBUDeDFwkJF2/no7PfBblCjtcqAXk9xwlFHg0V8PXI7hgZ9YTTZ9WAYBhadGk12fVZB9kQojiO9XmxfXiP7+dSYSMn07D5/ZpZjA6TYe2YEr2A0gQFvGMsE4aiS/YMcx/FRNfP0rre0tBybQqFQKJTchGJEEFFcNeXYvZ5gzok0uSCOI61Mx9GqJiv+dueyop+bXGrMWnGSmtxibAD47M2LsarRiu++cg5bv70b33n5XKWe4ozAdxylvyfL6vk+i7kw+YYyP3jiUB+2fXsP+otYEM9FiONIr8kWjpbUmmHUKHGc9hyVxIFLblz7nT04MZD++0uyHA5c9ohuI4CPQkudw4Z8EejVSlj1athkLNQ9gnNHzgbHtB1HAz6sbrKhxWFAn0zHEcty6BzwYWOrQ/xatUkruxybCG0A0OG0ZEXV3rowjiTLYfvyOpmvAqgWxInZXpBNomqpU0drLboZcxwR1+/SOn5jy2aonHA0GU0gHE9Oa6IaAKhVDC3HplAoFAolF6Qcu86iuyqiakmWw7AvUnQHTrGOo5mAuI6IgCSHNc02PP7AVrz8heuxvsWOn/zxIhLT6K240khF1cgOJ3UdUcpNnzuEV04NI5kyTvy544P4+9+fxIg/IvalzFfIYlTKcaRUMFjVZMUxOlmtJM4LxysSSxO/PjKJyUgCmxdOiSd1Fi3cwViWkDM8EYbTxhcEW2R0HLmCvIggK6qmKl04CkYT6BqdxJpmG+84ktlxNOgLIxJnsazeLH6t2qyBS0Y59aAvjEZ7inDUYMFlVxChlIEgb5wbg8Oowdpmm+zXUm2aG8JRNJ6ERqVIK4uuNWtnrOOInH+XzIDjiPR9kQLwUqHl2BQKhUKh5IGUY9dZtFeF42h8MooEyyEYSxaVtRcdRzKmrswUGwXhqJioGqHdacG7VtQDKP+F3L6Lbtz384MzIkhF4mxW7xRZZJwbocIRpbx8f1cXHnz0KP7sB2/jj13jeO30CP7myU5sWehAg1WH/ZfchX/IHIacL3J1va1ttuPssL9iPSbzGeJWO9KbHvU70usBgDTXTb3grBjPEC+GJiJoELp6lAoGZq1KluNITlRNU2L/CwCcGpwAywFrm61odRjhjyRkRaW7hS7C1K7AGpOWL6fOc70SiCYwEY6j0TY1NKLdaQHH8UIcwG8ivXF+DDcurYFSIX8Sl02Y7DbbHa3RBJvlxq018zE/lq18rLZ7LACNUoFWB/8eiMJRgbL2UiCxzWk7jkp01c2eq0IKhUKhUCoIKceuteiuio4jMqIXALxFdPxMlWPPHsfRnWsa8KUdS7F1kaPwjSUg1nFfmYWjvRdd+GPXeM6+jnLBcRwiiWzHUY1ZiyqjBl1UOKKUmT5PCM0OPcLxJO77+UH8xaNHsKrRip/dtwnb2qqx/5J7RhZlV4pwPLs3JZV1LTbEkxxOS5QQU/LT7+GPl0d7vWlumsO9XtSatWhKcc+QBXJmXG1kIgxnSkGwRa+GP59wFCRRtcqWY58Y4IuxVzfZ0FLFCwlyCrIvCnGntpTNETmOn0EyUc2eHlUDgOc7h5FIsjje74M3FJc9TY1QzGS3K4mUG7fWrEWC5eCZgX5DcqxUKXlZRZzyVwHH0cgE/3dQP+2oGnUcUSgUCoWSkzCJqpl1V4XjaNA3daHtljGZhTAVVZs9lwgmrQpf2LGk5PiczcAvFspdkk0m3vRVuO8llmTBcdKL2KV1ZpyjUTVKmRnwhrF1YRVe/5sb8NAd7bhjlRO/+uRmmLQqbGurgjcUFyNH85FoPAmGyX0cXNPER37ODE1Ifp+Sm34vf7x0B2NposrhHi82LrCnR46ESM5YinAUT7IYm4zCmdLrYzPkH4E+FVWT0XGkUogbKMVyfMCHJrse1SYtFlTx0Wo5BdkXxwNwGDVpjihROMrj+CEbRKkdR012PXa01+Ln71zGu77/Fv77jW4oFQxuWCq/GDv1OciZ7HYlkRSOBGFlJuJqQ74wGlI/i3r+PaxEVG10sjxRNa1SQTuOKBQKhULJRTCWhFrJwGHkR6VOp/xyLkB2IoG57ziaLjYZHRilQHZiixm5XApS5Z+EZfVmXBidnNfuD8rMEk0kMToZQZPdAK1KiU9fvwg/+vB6WAXnHnH+zee4WjiehE6lTBMxUqk1a6FRKTDgrazbcD7S7wmJPUaHhbja8EQYg75wWkwNmHIcEacFwLuPOA5oSHEcWfXqvA4PTyAGvVopWXaeyXQ6jjr7fVgj9Ai1CNGlPhk9R91jAbRlDH+oNvMCRD7hhnz+mlMcRwzD4OGPb8RPP7YBHAfsPjeGDa120QlTDNUm7ayPqknFuGuFYu+xGZisNuhLLyc361RgmEp1HEVh1qlg0Kim9XPUSgXiieKvGahwRKFQKJSrgnAsCb1aCbOOv3ia73G1oZT4lKeIguwocRzJnKo2F7ALjiNvmYUjtyAcVXrCVDRPbGZZvRmhWJIuYCllY9jHL8xTI0OpNNkNaHbose/i/BaO8okMCgWDRpue/t0VyUQ4Dn8kge3La2HWqcSeo8M9/L83LrCn3d5h0ECtZDCaIl4MCyJSquPIqs/vOPIEY7JiagAv0JfiOHIFohjwhrGmyQqAn8hXa9bKi6qNB9P6jQD5UTWNUiHelsAwDHauqMerX7oB/373Gnzj3R3FvhwAfBx61kfVJGLctWbBcVRh0SsST8IViKU5jhRC51a+6GSpjPoj0+43Akrv8Zo/V4UUCoVCoeQhGE3AqFXBrON3auZ7XG3IN9UBUZRwRBxHs2iq2nQhTolyR9XcwZmJqhHHUS7hCMC8jg1RZhYihuQSjgBg26IqHLjskXS6cRyHX75zGXu7XRV7jpUmHGMlJ6ql0mjTY6DC/WbzjQEhptbqMGB9ix1HBeHoSK8XerUS7UI/D0GhYFBr1qV1HJFNkdSOo8JRtZisiWoALxyV4jg6McBP2SMxRgDCZLX85wdPMAZPMJbWbwTwGx4KJr9wNOALo8GmgyJH6bVaqcAHNjRhRYNV7stIo9qkhVvGZLcrSURwB6ZColyVdksRETPVcQTw1xzlvt4AgBF/BHXTjKkBxHFEhSMKhUKhUCQJCTvIJi0vHAWi81s4GvSF0eG0QMEUJxxFhG4PtVL+9JXZjlmrgoIpf1TNPUMdR5FE7glPS4URwOdHaEkvpTyQDpomhyHnbba1VWEiHMeZ4fTPXZLl8H+fOYl/fP4Mfrm3p5JPs6LwLob8y6RGmz4tEkwpDCnGbnYYsKHVjq6xSUyE4zjc68HaZhvUyuzfea0lfbS66DjKKMeeCMdzChyeYFTWRDWgdDdGZ/8EFAywsnFKpGlxGNHryR9V6ybF2BmOI6WCgcOY3/Ez6A2nFWOXm2qThp/sFp6910uROJvlkNaplTDrVGndWJWAiJgNGcKRTa+pWFStHI4jtYpBlDqOKBQKhUKRJhRNwKhRiVE1/zyPqg36wmh2GGAzaIp2HOXr9piLKBQMbAYNfOHy7QBG4kkEogkwDNDnDlV0R5YUlku5wExaFZrsepwfDVTs8SnTxxWIzvquEMKANwSVgkGdOffO9tZFVQDSe46iiSQ+//gxPH6wH1qVYk67OiOx7PhLJo12PVyBqPj3SSkMcRw12fXY0GoHxwHvdLtwdngyK6ZGqMtwHB3t9aLBqhPP5QAfVYslWNGdmQrHcRifjMIhoxgbKG2qWiiWwPOdQ1heb4FRO9U/01plwKg//2fk4jh/7F6c4TgCeOFmfDL3eSuzX6fc1AjHgPFA5buCCBzHFdXZJ1WODfA9R6VE1ZJFPDaZqJrlOCoQnSwFluUwNlmeqBopxy72uoUKRxQKhUK5KgjFeMfR1RBV80fimIwk0GDTwWEsTjiKxJPzqt+IYNOry9pxRGJqS2pNmIwmyu5mSiVfVA0Alteb0xxHSZaji9lZxt891YkvP9V5pZ+GLAa8YThtOnG8tBROqx4LqgyicDQ2GcGnfnkIL54cxkN3tOP6JdVzWpwPx5MFo2okyjdE42qy6feEYNaqYNWrsbbZBgUD/O+fLiPJctjQmkM4smgxIghH4VgSb10Yx60ddWm3IcXPUov1s8OTGPVHsbZZXlyrlHLsbzx3GpfdQTx0Z3va11urhILsPK7U7rEAdGqFpACUr2MoEk9ifDKKJntuZ+B0qTGRyNfMTVb726c68eCjR2TfPppgJQdH1Jp1RQtHPa4g2r/+Cjr7fbJuP+gNg2GAemu6mFMJ4cgbiiGe5PIK+nJRKxXguOJEMoAKRxQKhUK5SgjFkjCmCEeBeSwcDYm7YAY4inUcxaUvwuY6NoMaE+UUjoSL+fUt/GKnknE10XGUQ9BbWmfGpfEgYgkWSZbDZx45jA/+ZG/Fng+lePq94bTJULOZAW8YTbbCi9FtbXzP0R86h7Dze2/hUI8X//rB1fj09Ytg0anntHAUKVCODUy5DAapcCSbfm8YTQ4DGIaBUatCu9OCI71eMAywPpdwZNVhMpJAKJbA2xfGEYmzuLWjPu02+UagP3t8ECoFgztXN8h6jsVG1Z49NoinjgzgczcvxjVt1WnfI5PV8vUcXRwPYFG1SbKnqNqUWzjK1a9TTqqnOZ1sMhLHI/t6inIQdfb78E63S/Z9orkcRxZt0c/7wGU3YgkWp4YmZN1+yBcWJyymYqmAcDQqxDXLVY4NoOhI5vy7MqRQKBQKRYJgLAGDVnVVTFUjvRulOI6iEhNK5gM2gwbeMpZVkov5dS18EWolhSOxsDzH+7Ks3owEy+GSK4D/3HMBe86NoWskMKsLTa823IHonBFSBryhvMXYhK2LqjAZSeDzjx9Dq8OAlz5/He7e2AyAXzjN5l6UQoTjrKyoGgDac1QE/Z5Q2uh44jJaVmeGRSc9Lr6OTMjyR/H6mVGYdSpsWeRIu00ux1GS5fDc8UHctKwWdrkdR0qlbMfRZVcQX33mJDYvcODztyzJ+n5rlREA0OvO3XPUPRbImqhGqDZp4ApEJY/l5JxTyY6jJrsedoMa/++V8+jLEL8mQnHsu+jG00cG8P1dXXi+cyjr/o8d6MPXnzstW4jhOA4D3jCCsSR6ZZ5TIwlWclOl1qzF+KT07y4XJwf55znskyc4DU2Es/qNgKmy9nKeg0lcs7YcHUeCmzSeoI4jCoVCoVCyCMeSMKivjqjaUEru3m4sTjCJzGPHUTnjZC6hGHtt85V3HC2v5ycR/ezty/jB7guwG9SIJdmyRvMopZMQ3otKjGcuN9FEEqN+efGXG5bUYFWjFV/euRS/+8trsLjWLH7PolNhMhIvymkwm8jVm5JKvUUHpYIRp9BR8kNEgeaU0nUiHOXqNwKmHBZDvjB2nR3FLctrs0q0iXCUOclq/yU3Rv1RvG9do+znqVEpEE0UjvpyHIcvP9UJtUqB739orWS0025Qw6xV5Tw/hGNJDPrCWRPVCNUmLSJxFsFY9vN54lAfTIJrq1IYNCr8+v4tCMYSuOen+9DrDiIST+K/3ujGtd/dg3sf3o8vP9WJ7++6gL97uhPBjKEjr58ZBSBfXB2fjIobJadlik1SU9UAPqoWibOYLGIQyslBPvI9NCHv+Q75IpLCkVWvRjzJIVzGyDgRjjJjcaWgFq7xosnint/8uzKkUCgUCkWCYDQBo1YFtVIBnVoxr6eqDfjC0CgVqDZpUWXUwBuSv4Cbt44jvaas43HJRLVmhx7VJm3Wbmw5IcKRVuLiGAAWVhuhUjB4+sgAltWZ8dAdHQAwZ6JR8x2P8LkLxpJIlDDJZiYZEnba5TiO7EYNnv/cdfjr7UuyFs1mnRosxzs95yLhWBL6Al1vKqUC9RYdjarJxB2MIRxPpjmOti6qgl6txPbltTnvR8aPv3RqGN5QPCumBuR2HD1zbBBmrQq3tOf++ZloVQrEk4ULmo/1+3Ck14u/vXWppHgAAAzDoKXKkDOqdskVAMchj+OIf+2ujK6eM0N+vHRyBJ+6doH42ivFykYrHvv0VkTiSfz5/+zD9n97E//66nlsa6vCI5/ajD/+3U34zae3IBJnsfvcmHg/dyCKI31eAPLjnP0pAtOpwcKTQjmOy12OLXxuUify5SOeZHFWmBIpx3HEslzOcvJ8nVuZvHBiCPsuugvejkTVSO/UdNASx1GSOo4oFAqFQskinNJZYdKq4Z/XjqMInDYdFAoGdqMGSZaTHZOZr44ju0GNYCxZdOlpLtyBKAwaJQwaFVqrDJV1HAnPOVdpuUalwOJaE0xaFf77I+uxoJqPR4xWeBQxRR6pUdHZ7nRMnXo1HSx63tk5V4+zkUThcmyAd3XSqJo8+oVjZKrjqM6iQ+c3dmL78rpcd0Od4LB45uggNEoFblxWk3UbqyF7oR6OJfHKqRHcvqq+qM0Quf0vv9rbA7NOhfevb8p7u3znh+4xfqJaW61R8vukYyiz5+gHu7tg1qlw/3WL8j52uehosODxB7YiyQJVJi1++8BWPPzxjbhhaQ1aq4zYtqgKdRYtXjwxFVfbc24MJKkl15VHjj8mrUqW4yie5MBy0m7cmiL7mS6MBhATiraHZTiO3MEYYgk2r3BUyOXMshweevYUvrerq+DjjU5GUGXUZPUplYJaxfdpxYu8HlIVvgmFQqFQKHObWIJFPMnBKAhHJEYxXxlK2QWrEnod3MEYbIbCHQ/RRDJtnPB8wSYsLHzhGGrN07d6u4MxVJn432eLw4CDlz3T/pm5iIpRtdyLn2+/fxUUDINFNSZxd3eECkezAuJOA/iJh3K7Vq4EZIHX5JjepCbSV+MPxyta3lspwjF5zstGu76if/vzCeImyYxBFloIm7Uq6NVKBGNJ3LSsBiaJ85NZqwLDIC0OuuvsKALRBN5bREwNgLhxEkvm7rkam4zgpZPD+NjWBQXPly0OI147PYpEks1y5l0cD0LB8K5RKaqFc0yqcHRqcAKvnh7FF3csEQWzmWB5vQX7/mE7VAoGDJNe5K1QMPizVU785kAfAtEETFoVdp0dhdOqg0mrkj15kIiLNy+vxd5uFziOy3qsVCKJ3OdGcp4flzlZ7ZTQb3TD0hq81TVe8LHJa5LsOJLpOLrkCsIXiuPssL/g4435I2XpNwL4Hi+AlmNTKBQKhZJFWOgH0Gv4CzyzTjWvo2qD3qnCRrJI9cosyJ6vjiMimpVrsporEEWVkd/RbHYYMDwRLpubKROx4yhHVA0A1rXYsaaZL+quNWvBMDSqNltIXfRdCcdRIJrAQ8+elLWA6veEoFIwqJ/mAsWinxKO5hosyyGaKFyODfDOrBF/ZNZHEGcDRBQo1s3GMIwYV9spEVMDeOHColPDl/J5e/bYIJxWHbYurCrq8UTHUZ7j+WMH+pBgOXx8W2vBn7eoxogEy+HieHZB9sWxAFochpwxZBJLSv3b/f6uC7DoVPjUdQsLPna5USsVOcWNO1c7EUuw2HVmFJF4Em91ubCjvQ6Ndr3sqNqAN4xqkxYbW+1wB2NiPCsXYoy7DFG1E4M+mLQqbFtUhWiCLThUZEo4yj5WWmQKR0eFKN9kJIGhAufrEX8E9Zbpx9QAQK3k38Nir1nm35UhhUKhUCgZkJ4N4jgy6VSzPjJSKvEki9HJiKTjSA7RRFLyImyuQxxH5SqMdgdi4m5wi8MAlqvcWO5InIWCmbrYK4RaqUCVUUujarOENMfRFRBS3jg3hkf39+Hn71wueNsBQXRWSowGLwbRcTQHj7OknJdEm/PRaNMjyXLU3SeDAW8IVUZNSY7WWosODAPs6MjdVWRNGYHuCcbwx65xvGdtg+SY+3xolPmFo1iCxW8O9OGmpTViLDgf1y+pBsA7oDK5OB7IWYwNAA6jBgwDjAvHkM5+H3adHcWnr1+UcwrdlWJdsx1Oqw4vnBjG3osuhONJ7Oio4+OcsjuO+ImOKxr4wm/iAspFNC7EuCU2u8xaFXRqheyo2slBP1Y0WMRNt+ECQs5gyhCSTOR2HB3t9Yr/fXYof6fTqD8qFsVPF7XMOGYmVDiiUCgUyrwnJDqO+IWAWauet1G1kYkIOG7qYoY6jnjsguOoXAXZqY6j1io+elGpniNS/pnPxp5JvVVLF7MpcByHvd2urKk/M4E7OLXjLbdrrJwcERYnTxzqLzgtakBYuE0XseNoDjqOyCQkWR1Hwu+K9hwVZsAbLjkCeW1bNe5Y5cwbMyYj0AHg1dMjSLAc3rOmoejHKuQ4evnUMMYno7jvmgWyfp7TqsfqJqs4YYyQZDlccgVzFmMDfAG73aCBKxDFqD+Cv/rNUVSbtPjktfIeeyZRKBjcscqJt7rG8fujgzBqlNi6yIFGux6+UFzWsZdM3Wt3WsAwwOkCYkokT4ybYRjUmnV4+sgAtv/7m9j4T7vw2d8clfw5pBh7VaNVdBAVitcN+sIwapSS5eRi51aBjaqjfV5sEiYKnhvJ/VoTSRauQLRsUTWxHJs6jigUCoVCSSckOo6mompyHEcsy+Hun+zFq6dHKvr8yom4CyYsaByGYh1H8iIacw25ZZVyYFkOnoyOI6CCwlEJk+7qzDoaVUth30U3PvyzA9j67d34pxfOVHQKXibuQAxE8/OHZ164Otzrgd2ghicYw0snh/PedsAbLo9wJDqO5q5wJFW4mwkR6OlktcL0e0JpE9WK4Qs7luBHH16f9zapjqMXTgxhYbURHSWMqifCUTTHovpXe3uwsNqIG5Zkl3TnYmdHHY73+9JcoO90uxBLsFjVZM1732qTBj2uIO77+UH4QjH84hObYJ5lbiPCHaudiCVZvHBiGDcuq4FWpRT/RgoJMUmWw5CPP/4YtSosrDYWLMgm75Eux2bXR7e2YGWjFe1OCxZWG/DiyWHJ4mtSjL2qyQqnVZ7jaMjHuzOlNnTMWhWUCiav42giHEfXaADXL6lBi8OAs8OTOW/rCsTAcVMTBqcLcRzRqWoUCoVCoWRAHEeGlKhaQIZwFIglcKjHi0NzqPyU7HwTu7Veo4RerZTtOIomkvPTcSQ4r3zh6TuO/JE4EiwnjkquMWmhVSnQ587usCgHkTib88I4F3VWHcZkloJm8uyxQRzv95V039lKjyAUbVrgwC/39uCmf3sDey+6ZuSxXYEYGoTFyEwLKYFoAmeG/Pjo1lYsqjbi1/t6xe9xHIdH9/eiU3ivI/EkxiajWeXFpWDWEcfR3IuqkU48OWItOc7KnRp1tZIURpc3T7N0PR8WvRoToThcgSj2XXTjjlXOolyaBNI3JOU4iiVYHO3z4d1riovA3Sp0M6XG1X61twfVJg1u7cg9UQ4Aqk1a7L3oxsXxAH7ysQ0FhaYrydpmmygU7WjnXxf5/4ECwtGIP4J4kkOzcPxZ0WCdluMIAB64oQ2/vn8L/uvD6/GdD6wGALxyKnsjkETiVjVa+cllSgWGCkxWG/JFJIuxAd7tZNGpROFofDKKa7+zB291jYu3IefYDa12LK8342wexxFxD9eVYbAHwMfZASCWzO9AzWT+XRlSKBQKhZIBcRwZtMRxpEYglgDL5t9tITELuW6d2QDZ1XNapy4wHEZNwaJHQjTO5izqnMsYNUqoFExZOo5cQt8EcRwpFAyaHblHLk8XElUrhnqLDp5grGA0KZMXTwzji08cx1efOVnU/WY7IxNhKBjgpx/bgD/9n+0walR48UR+9025cAejaK0yZE19mgmO9/nAcsDGBQ58ZGsrjvb5xEXSj/Z046FnT+EvHz2CUCwhHjvK4ThSKRUwapQVFcr6PSEc7im/qB8pIqqmUytRbdLSqFoBRgVRoByfrVwQx9HLp0bAcsCda5wl/RwxqiaxqCZR51pzcc6PpXUmtFYZ8NppXjjqc4ew5/wYPry5peD5ljzWv929BtcX4XK6EjAMg/esbYBGpcDNy/g+KuJ+IsNLaQAAIABJREFULuQ4GhDOn80O/vYrGiwY9IXzbnpFhI4jOefHthoTltaZ8LKEcHRycAImrQoLqoxQKBjUWbUY9slzHOUi0wE36Avjv97oFr9/pNcLBQOsabah3WlBjysoitaZEKdavbVcU9VIHHMGHUcMw/QwDHOSYZjjDMMcFr7mYBjmdYZhLgj/tgtfZxiG+SHDMN0Mw5xgGCa/35BCoVAolDKR6Tiy6FTgON5RlA8SZ0udijTbGfTxU0lSL6QcRg08Mrp9WJYTRhDPv30lhmFgM2jKElVzC58H4jgCgFaHAX2eypVjF1tYTqZiyZ0oA/BFrV95uhMGjRKnh/zoHsttnZ9rDE1EUGvWQaVUoN6qw6aFDuy/5J6Rx3YHYqgxa2HWqma8LPpwrwcMA6xrseGD65ugUyvwmwO9+O3BPvz7613YtqgKQxMR/GhPt+iaKYfjCOAdIJUUyv6/50/jIz87UPZIpigcySjHBlDU1KirFTJRrblMny0pbMJC/YXOIbTVGLGszlzSzyGLaqmoGtl4IMMW5MIwDHZ21GHvRRcmI3E8sq8HSobBR7YWnsr219uX4Bef2IS71jYW9ZhXii/csgQvf+F60eVba9ZBpWAKiqv9GccfUpB9Zji3EydSRKwUAG5bUY9DPZ6sCZMnBiewosEiusicVr1kpC31cd3BWF4h1KqfmvL3h84hMAxw4LIHZ4XXc6zPi2X1Fpi0KrQ7zWA5oGtU+pxLxDOH8DudLhqVMFXtCpRj38xx3FqO4zYK///3AHZzHLcEwG7h/wHgdgBLhH8eAPDjMjw2hUKhUCgFCUUzomqC86hQXG1KOJo7jqNBXxiNGeNh5TqOyIXyfHQcAfzFfjnKsYkDjTiOAKDZYUC/JwSOK24HTw7RRLJoMa9O2JmUW5AdiiXwl48egVatxG8f2AoFAzx3fKjo5zpbGZmIpO3WblnowMXxoOyJO9PBLRSpV1pIkeJIrxfL6syw6NSwGtR479pG/O7oIL767CncsLQGj9y/Ge9f34iH376Ety/wMYpyuUIsOnXFHEfxJIt9F92IJlj8554LZf3Z4QLxl0yaqHBUECIKVDKqZtWrkWA5HLjswR2rG0qKqQH5y7G9wvmDDFsohls76hFPcnj51AiePNyP21c5ZU3JWlxrws3Lc0+Tm23o1Mq0SXFKBYN6q67g38iANwSGmRpvv6KBj+Tlm6wWSRT3t3rbSic4DmlF5anF2IQGqw5DeRxH5LU02HK/f1aDBhPhOPo9IRzr8+GB6xdBp1bgkX09SLIcjvX5sL7FBgBoF7q4zuYQyQJCsXgpEwml0Cj539dsKMe+C8CvhP/+FYD3pnz9EY5nPwAbwzCleQgpFAqFQimCYFY5Nr9bWKggm0xec88lx5E32z4tXzgqbvdurmE3qMviOCIONDJVDeALsgPRhOxIYDFE4knoihTziONIrhvjoWdO4cJYAD/80DqsbrLhmrZqPHd8qCJC2JVgaCKcdpG/dVEVAODApcr2l4VjSQRjSVSZNBUVUqQgi5ONwtQeAPjo1lbEEixWNFjw44+sh1qpwD/c3g6dSomf/ekyVAqmbCOfLXpVxTqOOvt9CMaSWFRtxBOH+tPKzr3BGH69vxfJAlHkXJD4i5yoGgA0CePGC0Wfr2ZODU5Ap1ZIji4vF6nTre5cXfoSU5tHOPKV6DgC+C4bh1GDf3rhDPyRBD5xTWG30Xyh0aYv7DjyhFFn1okbVw6jBg1WXd6eIzGqJvP82O40o7XKgJdPTcWUU4uxCU6bHqP+SM5jCIndke46KazCRsELQiT6o1tb8d61jXjm2CAO9XgQiCawoZU/NjfbDTBqlDg3Iu04Iq55o0wXZCHUguMoPsOOIw7AawzDHGEY5gHha3Ucxw0DgPBvIpE2AuhPue+A8LU0GIZ5gGGYwwzDHB4fH8/8NoVCoVAoRUNOuiR6QIpbJwss4oiw5AnG5sSiYHwyikuuIFY2ppdnFhKOIvEkdp0ZxTefPwNA/u7dXMOq14g7xtPBJUzJsqcsHio5WS0SLz4+SISjURmOo67RSfz+2CA+e9NiXLekGgDwnrUN6POE5kVJNsdxGPZFUG+Zushf0cBHBA5crmxczR0ksUZNRYUUKc6N+BGIJrBpgUP82spGK554YCt+ff8Wcfe6xqzFl25dCo7jy56VRZT+5qOSQtmful1gGOAnH9sApYLB93d1AeBF/nsf3o+vPXsKJwZK++wW6zhqtOsRS7BwBefOBsNMs/+SG5sWOEQ3TyUgwtHSOhOWlhhTA1I7jqSEo9IdR0oFgx3ttfBHEljZaMH6FnvhO80TGu36gh1H/d6Q2G9E6Giw4lSeyWrFRtUYhsFtK+ux76IbE6E4IvEk/vXVc2AYpL0fDVYdEiyXs6ZAFI7ydhzx5djPdw5hbbMNzQ4D7rtmASJxFl9/7hQAiMKRQsFgWb05ZywvGE1Aq1JApSzP389UOfbMCkfXchy3HnwM7bMMw9yQ57ZSZ6Gsq3CO437KcdxGjuM21tTM7gIwCoVCocwNQrEElApG3EkUhaOoPMdRguXyjlWdLbzTzU+JyhwT7DBqEIolxYssjuNwuMeD/9x9AR/92QGs/eZr+PQjh/H6mVG8Z00Dts8hW3wx2A3qsryP7kAUDoMm7SLOKbhZCo3wLYVSyrEtehV0aoUsx9GTh/qhVjL45LULxK/dtrIeGpViXsTV/OEEwvFkmuNIpVRg0wI79lfYceQmRepG7Yw7jg73eAFMLU4IWxZVpbkzAODj21qxstGC5fWlL7gzsegr93rf6XZhVaMVS+vM+MQ1C/DM8UHsu+jGhx8+IPaElDrpLBIrbjFKXDS0IFsadyCKcyOTosuvUlgFIf+OVQ3T+jlix1E8d8dRKcIRALxrBT9d7b5tC0qO0s1FGm16YWpabqFi0BvO6sDatMCOS+NBDHilN2TINU0x8frbVzqRYDm8cHIIf/HrI3jj/Dj+5X2r0mKUTmv+Qu9BXwQKJn9ZtVWvhicYw5lhP969hv9Mtjst2LzQga7RAKqMGnHDCQCWOy04N+yXdPkGogmxYqEc5Itj5mNawhHHcUPCv8cAPANgM4BREkET/j0m3HwAQHPK3ZsAzP2rEQqFQqHMekKxJAxqpXihNuU4yi8cpRbZuufAbvJbXeOwG9RiqSSBFCoS19FThwfwwZ/sw7+/3gVXIIoPbWrBr+/fjCNfuxU/vHdd2aIqsw2bQV0Wx5E7EEvrNwKmFo+FdlVLIZIoXjhiGAb1Fl3BjqNYgsXvjw1iR3sdqlLKvi06NW5ZXosXTgwjUeSu5GyDjFXOvMjfsqgK3WOBrKLUcuJJ6cOa6Y6jw71e1Ft0suJBKqUCT/3FNfjhvevK9vgWXWUcVoFoAsf6fLh2Me+Oe/DGNhg1Knz4Z/vR6wniJx/dAAAl9w6Fi5iqBkxNjSpVqJrvHLjMi7OVFo5WN9nw/nWNuHdzc+Eb50Grzu840qgUJce5ty+vxWOf2YIPrG+a1nOcazTa9GC53NHpeJLF8EQ4q19tpyC0kWl0mYi9jEW8H2uarHBadfj6c6fx1oVxfPcDq3Dv5pa020htBP3zi2fw4K+P4Ae7LuDgZTfqLDrRuSOFTc9fIzBMenTyE9csAACsb7WniYftTgv8kQSGJH5HoVgSBm35nOCamXYcMQxjZBjGTP4bwE4ApwD8AcB9ws3uA/Cc8N9/APBxYbraVgATJNJGoVAoFEolCUXTT7pTHUfyomrA7C/I5jgOb11w4bolNeJkEALZHSWL2GeODaKtxohjX7sVr3zxBvzje1bg+iU1FY0RzAZsBg0icVbcpSwVdzCa1m8E8LuLerWyQo6j0ibd1Vl0BaNqu8+OwhOM4c83ZS+27lrbAFcgin0zNH2sUpDFijOjj4IsZA9erpzryJUygY93HM1cVO1IjwcbFthlOxv0GmVZY6oWvRqTkXhJMd983VoHL7uRYDlcJwhHdqMGn9u+GEaNCr/4xGbsXFEPq15dlAPo7Qvj+PW+HsSTbPFT1YjjiBZkS7L/khsGjRKrm6yFbzwNTFoV/uOetaid5sbH1Khy6XJsu0FdsluIYRhc01addY6e7xBxNdfGyrAvApYDmjLK0xdW89PxXj09Inm/aDwJhpnqpZIDwzC4c7UTLMfhux9YjXs2tWTdpiHDcTToC+Phty9j/2U3vr+7C/svebCgypj3cYirc8tCR9pm3M6OOly3uBp3rU13xnU4ebfnOYm4WiCaEDs6ywERvOKJ4o7N03kGdQCeEf5wVAAe4zjuFYZhDgF4kmGY+wH0AbhbuP1LAP4MQDeAEIBPTuOxKRQKhUKRTSiehCHlpEscR4Wnqk0JS+5ZLhydG5mEKxDFDUJHTSrEHeMJxuANxnCwx4MHb1wkjsu9WiCFpr5QHPXW0hfI7kAMHRmuLoZh4LTp8o7wLZVIPFnSpLt6qw7H+vL3vDx5uB/1Fl1WvBEAblpWC7NWhWeODeJ6ie/L4f8+cxI3L6vFrR11Jd2/HBDHUeYEnJUNFhg1Suy/5MYd0yjTzYc7ZYyyRa9CIJpAkuXK1iOUiyFfGEMTEXym9cr1qFh0arAcP5yAiPVy2HfRjc//9hg+f8sSfExiXPk73W5oVYq0CN5f3NiGT167UBS/G23yJ51xHIeHnj2FXncIj+7vQ2sVv3iVW7hr1qlRa9biWJ9X1u2vNvZd5PuN8rkzZhP5p6rFS46pXc00FBBX+4UomtREx3etqMOP3ujmp1Oa0jdsIgkWWpWiaCHvy+9ahns2tWBxrUny+zaDGjq1QtwIevkk73V55q+uRa1Zi3MjkwWdnBZBOCIxNYJKqcCjn96Sdftl9VOT1W5pTz9fhmKJsk1UA/i+LaWCmblybI7jLnEct0b4ZwXHcf8sfN3NcdwtHMctEf7tEb7OcRz3WY7j2jiOW8Vx3OFSH5tCoVAolGIIRRMwpOwe69VKKBWMrKiaWThZ5ypJnC2QUdpSC3xyoesNxbDn3BiSLIedHfUz+vxmA8Q67gtPTwR0BaKozriABfhdynwjfEslGmdLcoLUCVG1XO6NkYkI/tg1jg9uaJIUMnRqJd63vhHPHR/C+RzTXvIRiSfx2IE+/O7IQNH3LScjE3wfRU3Ge6ZSKrBxgQP7K+iocgei0KkVMGiUsAjiSSHBuhwc6uFdVBtbHQVuWTksev7YWYzL6vUzo7jvFwfhDcbwzedP4+RAdjHuO90ubFrgyPqbSHVMNtn1OXtRMrk4HkCvO4T3rWtEIJrAa2dGoVEpinKFvHddI3afHato7HEuMj4ZxYWxQMVjauUkXzn2RChe0kS1q51CPWD9wlCJzI4jgI+rsRyw62x2XK2U/j+A70TKJRoB/EZQg1UvbgS9fGoE7U4LFlYbYdSqsKHVnrffCAC2tVXh/usW4q61WbPAJDFpVWhxGHB2OPtcG4gmyyocAYBaycx4OTaFQqFQKLOeUCyZJhwxDAOTViUjqhZHs8MABcMvAGczb19wYWmdSfJipkpwFrkDMbx+ZhT1Fh1WNVY2NjAbIVPQvMHSe2aiiST8kYT4O03FaS2/44hlOcSSpUfVYglWHCGdye+ODoDlgLs35u7b+OKOpTBpVfj6c6fyxoekIAv3syO5xynPBEO+COosOsmJNFsXVeHCWKBiwrA7EEOVUQuGYcQd6HIWRp8d9ksKX29fcMFmUGc542YSIpTl6nXq94Twt0924ge7LuC10yN4dH8vHnz0CDqcFrz+Nzei2qTF5x4/ikDKEIOxyQjOjUyK/Ua5aLTz47/lfGZfP8PXsX7ltmV47Us34MEb2/AhiehmPu7Z1IwEy+HpKyyS5qPYItxyQD6b29rmkHBEyrFzRtWo46hYdGolqk0a0f2ZyYA3DKWCgVPi+mVFgwWNNj1eleg5isSTsp2BxeK06TDki2B4IowjvV7csaq4zTarXo2v3dlRVKn10joTLo4Hsr4eiiZglBmdlYtGqZjZcmwKhUKhUOYCoVgiLaoG8HG1Qo6jyUgCNoMaDqMGrjzj7K80kXgSBy57csaJrHo1FAwwPBHGH7vGsaOj9qrrWACmpu5MTMNxRHqiqs3ZjiOnTY+xyWhZF2hk8VLKrmq90KsgVZDNshyePNyPrYscaM3T1eAwavB371qGA5c9+ENncTNN+j38IqHXHUpb/M80wxPhnLvDWxfxjpz/eL0L//LSWXz2N0fxjedO4e0L42V5H13BGKqFqKhFiMiWc0LjP/z+JB589Eha5IDjOLzVNY7rFldXPBKXD1Eoy/F63zg/ht8dHcD3dnXhgV8fwUPPnsK2RVX4zae3YGG1ET/40Dr0eUJ46JmTogC07yIvRFxXSDiy6RGMJWX9rnedHcXKRgucVj2MWhX+/vbl+OZdK4t5qWirMWHzQgeeONRXtMA6E/S5Q7jmO3vwD78/MaPPb/8lN0xaFVZeQQGzWBiGybmo9obisFHhqCQabPqcBfL93hCcVmlxn2EY3LayHn+64Mo6j5Ta/ycHp+A4euUU3690+6rKxJlTsRs0ksesYLS8UTWAd9YV6zgq7zOgUCgUCmUWEool0WBLX3ibtCpMFljMTkbiqK42osqohWsWRxAOXPYglmBxvUS/EQAoFAzsBg1ePDGMcDx5VcbUgNTIXukL96nx6tmLhwarDhwHjPojaaN9pwMp6tWVUFxeb+XFrRF/BO3O9IXbJRcfz/nLG9sK/px7N7fgt4f68C8vncUt7XWyd1D7PFNRofMjfmy4QrGpkYns109Y2WiFw6jBYwf6oFEp0GjjFwu/2tcLs06Fm5bVYmdHHW5aVlNUTw/BHYiKxajk/uVyHI1PRtE54APHAQcueXCd8Pd/bmQSY5NR3LC0tF6qciE6jnII9ERQ6vz6Tlx0BTDmj+Lm5TVin9fmhQ58ccdS/MfrXXAFeAHu3MgkrPrCTqqmlEln+Rb6rkAUR/u8+MItS4p+fZncu7kZX3qiE/suuXFNW35hayYJRhP4zCOH4Q3F8PjBfqxusmVNkaoU+y65sXmhQ1IQmM1oVApEE+lDFDiOgy8Uo1G1Emm06XF+VDryPOANS8bUCO9aUY///dNlvHl+DHeunuoMKjWqJocGqw5jk1H8oXMIy+vNaKvJHW0rF2adWjLKHIgminIuyUGjVCBOHUcUCoVCoaTDR9XST7oWnVrWVDWzTo1qs0YsuZ2NvN01Do1KgS0Lc8cB7EYNhiYiMGtVc6pvopyklmMXomt0EmMSTh0Sacos6QR4xxGAsk5WiwiLl1I7jgBgVOL5EAGsKc/FOkGpYPCtu1Zi1B/Ft54/g0vjAVmTsvo9IZDO0jMSvQ0zAcdxGMrjOFIrFdj1Nzfi4Fdvwblv3oY3vnwTjn1tJx7++EbcvrIee7td+Nzjx7D+W6/jrx87WrQLiY+qCY4j0vlTphH1b5wfA8fx78/Lp6YGFf+xi+87kyo8n0mmXq/035s/koBerYTVoMb6FjtuW1mfVQL/2ZsX4+PbWuELx3C0z4cRfyRnJ1cqjTb+c12oIHvPOf53uKN9+uXtt690wqJT4bcH+6f9s8oFx3H48lOduDA2iZ9/YhOuX1KNb/zhNE4NZndHlZtRfwSXxoPYNgfPNxpVtuMoEE0gwXJi5JlSHI02PYZ82fFRjuNw2RVES57Nlg2tdlQZNaL7hxBJsNBWSDhy2vTgOOBYnw+3r6y82wgATDoVArFE2vmV47isuoVyoFYpii7Hpo4jCoVCocx7+IkU6Sdds04lGeFJhReOVIgltDgxkH861ZXk7QsubF7gyDs+2iEsXm9aXptWIns1oVcroVEp4AvlFwE5jsOHfrofDICH79uI9S1T05uI4ELiR6k0ClO7ytlzFInzF3baEuz4tebcUTWfsJgnI4MLsa7Fjo9ubcGj+/vwxOF+GDRKrGyw4t1rnHj3mgZJV0efJ4S2GhPG/BGclRgxPBNMhOOIxFnJ7gyCI8M9ptcocWtHHW7tqEOS5XCsz4sXTgzjl3t70OIw4Cu3LZf12BzHwR2cmgRkkXAcJVkOnmAMNRLRx0LsPjsKp1WHdS02vHp6FN+8ayWUCgZvdY1jeb25YHlrpZF6van4w3FRXMqFUsEUHRsDpsZ/5yrjJbx+ZhQNVh1WlCFKpVMr8b51jXj8YD+8wZjsqZXRRBIPv3UJH9u2QPbfo1x+tKcbL58awUN3tOPGpTVY1WjFHT98Gw8+egQvfu56Mb5bCUi/0VzcqNBKCEdkw4FG1UqjwaZHJM7CE4ylbbwMeMPwBGNY2ZS7d1GpYHBrRx1eODGMWIIVr2H4iaOViqpNHT/vWD0zLm2LTgWOAwKxhHj8jCZYJFiuAuXYxUfVrs4rRwqFQqFcVQRjySxRxaxT5e1dSbIcAlHecVRl0oiCwWxjfDKK86OFy2IdwsXuzis4Fv1KwzAMbHp1QceRKxCDJxiDLxzHvT/dL47iBQB3MI/jyMovVss5WW0qqlb8bqNGpUC1SYNRCeGI9CgUE7v41l0r8cLnrsP/++Bq3LOpGf5IHF977jQ2//NufO7xYwjH0qMd/d4wWhwGtDstV0w4Iu8FeW+KRalgsHGBA//4nhX40KZm/PiPF3Hwskf8Psdx6HNLT+/yRxKIJ7mpjiOJzp+nj/Tjuu/uwdhkcZ+ZSDyJty+4sH15LW5f6YQrEMWRXi+C0QQO9XiueEwN4I+xQG6H1UQ4Li6Oyo3doIZerczZqQKQ3+E4dnTUFT3OOxcf2tyCWJLF748Nyr7PiyeG8W+vdeGllONMORjyhfEfu7rwvnWNuP+6hQB4kfS/PrIeo/4I/vmlM2V9vEze6XbBolNd0YL2UpHqfyHnDVqOXRqimJvhAjwhTE5ck0c4AoB1LTYEoom0Y2U0UdrEUTk0CA7iJbUmLK41V+QxMiHHzNT+zZBwXq1MOXZxfWdUOKJQKBTKvCaRZBFLsDBmRNVMBcqxiahk0alQbdJiMpoQF/GzicPC2O0ti/L3x9SYtVArGdy07MovKK8kdoMG3gKOo8uuIADg3+5ejY4GC/7qsaP43OPH8M3nz+C106PQqhSSF3FGrQoWnQpDBeIxxRCK8Z/DUi+O6yw6jPqz+7kmhEWQpQiHA8MwWNloxZ9vbMY33r0Cr3zxBrz4+evwgQ1NeL5zCHvOjYm35TgOA54Qmu16tDstOD8yKSveVm5G/Px74bRN333ztTs70OIw4EtPHIc/EkevO4j7fnEIN/zrG3jhRHZxuFuMNfILTbNWBYZJ7/w5M+RHNMHijZTfnRRvnB/DSErkcP8lN0KxJHa01+FmwUX48qlh7L/kRjzJXfGYGgColPzfSU7HUSRe1OevGBiG4Ser+aRFPYAXNiJxtiwxNUK704K1zTY8ur9X9uf9qcP8JLbzI+WNcx7q8YDjgE9fvzBNGFvfYsddaxvxyqkRJPI4DrrHAvjELw6Kx6BiSLIcdp8dw03Laq9oQXupSJVjk/MGjaqVBukwIudXwokBHzRKBZbX5xcYidMrdeMnGk+W1P8nh0abHhqVAu9Z01D4xmWC9OCl9hwFhWvRsjuOSijHpsIRhUKhUOY1IUHsycyHm4WOo1wTZkj/kVmnEh0Ds7Hn6GCPBzq1Aisb8u/WPXhTG371qc0lFfzOJ6wGtRjTysUlYRzuxlYHHv/MVnxwfROO9Hjw5OF+HO71YkWDJadDoUEoVy4HkXgS//TiWejUCiypK62Ys96iSxMcCBPhOBQML2ZMhxUNVnzj3R1QKpg0V5EvFMdkNIFmhwEdTgtCsSR6PbkX8ZWCOI4aSnQcpWLUqvC9e9ZixB/BRx4+gJ3fewtHejyoMmrwyL7erNuT40WVkXenKRQMTFpVmuOIFIi/fiZ71DQhyXJ44JHD+NQvD4mL2T3nxqBXK7GtrQomrQo3LKnGq6dG8Ob5cejVSmxcYM/582YSi16du+MonCh7NCuVRps+Z8dRPMnihRPDMGlVBUX3Yrn/uoW47Api19nc7ymh3xPCPiHSVW5X3tFeLwwaJZbVZbslblleC38kgaN9uSPYu8+O4s3z41kLfVmP3eeFOxjDzhVz0+Eq1XFEhCNajl0aS+tMMGtVYoSR0DngQ7vTXDBCb5cQjipZjm3UqvDaF2/AgzcVHiBRLkgBdmr/ZjBWGeFIW0I5Nu04olAoFMq8JhQlwlH6Kc+qVyOe5EsHpU7IJF5h1qmhESbCuANRNNqmvwAtJ4d6PFjXbC940dVo08+6534lsBvUBRdCl11BaFQKNNj0UCoY/Ovda8TvJZIsFHliLU6rrixRNZblS22P9fnwk4+ul1ViLUWdVYfj/dmLw4kw7/ZQlMENoFMrsbjGhDMpC99+Ly+INDsM4ufu7LAfC6uN0368fDx7bBCvnBrBjz+6HgzDYGQiAqWCKalDSIr1LXZ8fvsSfG9XF+5Y5cTX7uzAc8cH8e2Xz+HC6CSWpCzSSbw1tUPJolOnOXCIcPT2BRfCEpFagF9ExJMczgz78R+vd+H/3LYMu8+O4drF1eKi6baVTuw6y4+337rIUbHFVLFkvt5UJsJxtNVU7vPQZNdnddM9c2wAzxwbwpEeD4KxJN6/vjGrkHu63L6yHo02PX729mXsXJG/G+X3RwfBMMCNS2twrM8HjuPKFps72ufD2mab5ESz65ZUQ61ksPvcKDYvlBbOLozxAnopZe6vnR6BRqnAjbMgMlkK+aJqtOOoNFRKBbYsqsLei1PCEctyODXox/vWNRa8P3F6pTqGI3EWuhL6/+SyoMLnq0ykomqVcxwxiMap44hCoVAoFBFis890HJHFbK4OjFTHEYmakIlas4XJSBxnhvzYlOPCn5KNTa8p2HF0yRXEgiqDZMRCpVTkFVucZXIcfW9XF144MYy/v305bpvGRJc6sw7uYCy76DUch62Mbo+OBgvODE0JR0QQabYbsLjWlOVIqhS7z43hldMj2H+Jj3AOTYShvthfAAAgAElEQVRRZ9aWNS7z+VsW4+2v3Iz/+sh61Ft1+OCGJmiUCjx2sC/tdqQPqzqlD4t34PDHJJbl0O8NY0WDBdEEiz91uyQfzyt8XhusOvzPWxfxq709GPSFsaO9VrzNre11UCkYhGLJWbVYt+hVOYWHSkbVAL5TxRuKi+eAQV8YX3qiE73uIN6/vgk/+vA6/Mv7VpX9cVVKBT557QIc7PGgU0K0JbAsh6eP9uOatircsrwWE+F4wYENcgnFEjgz7E8r9k/FrFNj0wJH3ohkNxGOCkwfzYTjOLx2ZhTb2qrmrMNVo1RkLapFx1EFP7PznWsXV6HXHcKAsLFwyRVAIJrA6gL9RgDEIvfU4RaRROUcR1cCs8RAgYCw+WnSlnmqGi3HplAoFAolHVIsmCkctVbxDo5et7T7hOz4WHRqceHnmmUF2Uf7fGA5YPMCKhzJxWbky7FzRRQBPqpWqjOmwaqDNxTPKoouhjfOj+E/93Tjno3N+IsbFpX8cwCg2syLnpm9ThPheFljQu1OM0b8EXiEeFa/hxfPmh166NRKtNUYZ0Q4Iv1SRMQZ9kXKPl2MYRg0p4yOrjJpcdvKevzuyEDa+y7tOFKJi4LRyQhiCRYf3NAEs1aFXTniamSh9NU7OrCgyoh/fJ4vNd6+fEo4shrU2NbGT6+aDcXYhFyOI47j4C/zZzATsjlAJqsRkeR/79uEb713Je5c3VCxRec9m5ph1qrw8NuXct7mYI8H/Z4w7t7QjOVOvt/l3HB5eo5ODEwgyXLY0Jo7srh9eS26RgPol4iQchw3JRwViPZmcmEsgF53aM7G1ADecRSVcByZdSpJBxdFHte08UM89nbzrqPj/UIxdrOt4H1t+pmNql0JiOModXBLKEo2P8vrOJLq8SoE/eRTKBQKZV4zJRyln3RbhIVfX47elclotuModbIax3EY80fw5vkx/Peb3XjyUH/Zn3shDl32QKlgsK6l8EUXhcem1yCWZBHOUXSeSLLo84SwsLq0TiEyiWU6rqNf7e1BvUWHb7135bRjK2SaXuZUQBJVKxcdTn7HmIhD/d4Q7Aa1uIPKT1Yrb/mvFEQ4euXUMFyBKEb8EThnIKL54S0t8EcSaSXZ7kAUFp0qLUZq0atFUZpMY2urMeGm5bXYfW5UslCZLJScNh2+d89aKBUMVjdZUWtJF8T+6qbF+NS1CyseBywGi15aOApEE2A5VGyqGsBH1YApV+mb58fQ7NBXNB5HMOvUuHdLC14+NSIpzADA00cGYNKq8K4V9VhWz0ccz46UR1w90usFgLznBiI8vnE+23U06o+Ki1d/niESUrx2egQAylo6PtNoVcpsl2YoRieqTZOldSZUm7R45yLvrjwx4INBo0RbTeHzrUYYSkEcmBzH8VG1CpVjXwmkomrk79A0C8qxaccRhUKhUOY1pFjQkGHzterVMOtUOS/qyYnbrFPDoFHBoFGKU5IA4GvPncKj+6eiKSoFg/evb5zR3ciDPR6sbLCUPfs+nyE9Ce5ADAZH9u9t0BdGPMlhUYmLSzL2fcgXwSIZF8OZDE+E8VbXOP7qpsUFe6vkQNwuWY6jUEwUT8tBu1NY+A77ce3iavR7QmmunHanBc8dH8JEKC5GDspNPMli1B/Bn62qx0snR/D0kQEM+cK4JcWZUym2LHSgrcaIxw724e6NzQAAVzCWFlMDBAdOmBcHiGjd4jBgR3stnu8cwvEBX1a8yBcm05w0WFhtxE8/tgFVpuzOpm1tVaLraLZg0UlH1YgYYdFX7tjVaOM/fwO+MCLxJN7pduPujU1l6xAqxCeuWYCf/+kyvvnCGdEFpmQYmHUqmLQqvHRyGO9Z0wC9Rgk9lGi06cvmODra60VbjTFvH8+iGhMWVBmw59wYPr5tQdr3LoxNPY9iHUevnxnF2mYb6izldfrNJFqVArFE+uaCNxSnxdjThGEYXNPG9xxxHIfOgQmsbLTKjhLbDBrRgRkVhD3tPHIc6dVKKBVMWjl2Ltf8dNEqFYhT4YhCoVAolCnCOU66DMOgxWHI7TgShSP+VFll0ogdRyzL4cUTw7imrQqfv2UJTg/58a0XzmDIF0FLVfkW4/mIJpI43u/Dx7e2zsjjzRdI2WX3eCBN2CBcGueji4tKjaoJY9+HSnQc/e7IAFgOuHtjU0n3z4QIR5kTAfmoWvkuA6tMWtRZtGLPUb8nhBWNU70V7UIU5+yIH1sXVUbcGJmIgOX4omFXIIb//dNlRBPsjDiOGIbBh7e04lsvnMHrZ0axfXkt3IGo6FYkWPRTUbV+TwgKhnep2Q0aqBQMdp0ZzRKOvEGhlFdwiN0yh5wcvMMqDpbl0rrBiBhRyaharVkLtZLBoDeMg5c9CMeTuHlZ5UVEQoNNj/eta8RTRwZyTs0jIiPAi6/nyuA44jgOR/u8uLWj8Ofk5uW1+M2BPoRiiTRXLompKZjiOo6GJ8LoHJjAV25bVvwTn0VIl2PHaDF2Gbh2cRX+0DmEs8OTODvkx33XyL+GsRunpqKSDirtPHIcMYKwLOU4Kns5tlKBeCJ3ZF8KKhxRKBQKZV4jTqSQyIe3OAw4Pyq9w+uPxKFRKsT8fJVRKy6+z41MwhuK4/3rm9IWwb2e4IwJRycHJhBLsLQYu0hEAWPYL7mIvCRMXCs17kP6dIZLmKzGshyePMxPxWqtKk+chghHnhS3HMtymAjHxc6IctHhtODMsB9JlsOgL5xW6p3qSKqUcERiag02PT6ypQVf+O1xAPyku5ngA+sb8eM3u/GZRw6j2qRBOJbE9UvS+4YsOjUf02I59HpCaLDpoVEpoFEpsHmhA6+fGcVXbluedh9fOA6GQUWLpCuFRacGy/HOz9Si5Alh8VfJqJpCwcBp1WPQF8Yb58egVSkq9tnLxXc/sDrt/UywLAKRBPyROBQMg3UpIuHyegveOD+OaCI5rUlvl11BeEPxnMXYqWxfXotfvNODvd1u7EgRmrrHArAIzqhipqqRnq6dMkSr2YxU/4s3FJ/xKVvzEdJz9PN3LiOWZLG6SX7U3qbXiO7ZqOAIm08dRwAfSQukCEehWAJKBVN2gUytYmg5NoVCoVAoqZAuGymbb0uVAQOesGSvyGQkIbqNAH4yEinH3neJL3YksRBStN3jlnYvVYJDPXyHxcY85aeUbKx6NZrs+rQJYKlcGg/AqlenFRoXg1alRLVJU1LH0YHLHvR5QrhnU3PhG8vEZtCAYQBPSqFoIMb3y5Tb7dHutKB7LIA+TwjxJJcWhas161Bt0uDk4ERZHmtvtwsXxwNpXyMurwabHretrBffw5kSjmwGDfZ8+Sb88N512NZWDaWCwaqMaUEWvRocB0xGE+jzhNJ+Rzva63BhLCB2HxF8oRisenVZJ8PNFCSKltmTQxxHlRbDGm16DHpDePP8OLa1VUFf5rhHIRQKBjVmrfiP06rHkjozNrQ60kQjAFjuNCPJTpVSl8rRPn6SW75ibMLmhQ4YNUrsyeg5ujAWwJI6c86OqlzsOTeGhdVGWZ01sxmNSko4oh1H5aDZYUCzQ49njw0CANYUIxwZ1GLnW0RwHM034cisU6cdL4PRJIwaZdkjthqlEnFajk2hUCgUyhTBqHQ5NsA7jmJJFqOT2e4QfzieIRxNRdX2XXRhQZVBnNpTZ9ZBo1KgL8eEtnIwMhHBV57uxDvdLnAch0M9HrTVGCW7Tij54YuapYWjy64gFlYbp3WR5rTqMTRRvOPoycP9MGtVuG2Fs/CNZaJUMLDp1fAEpxxHE8KFd7m7hjoaLEiwHPYI06uaHekRsRuX1uKFzmF0j02vxyWWYPGZRw7j3187n/b1IcHl1WDVQ6tS4u4NfNyv0V75qBrBolPjPWsa8J/3rkPnN3biszcvzvi+IKSE4+jPEI6IyHQ54zjiDcXn7Ahw4ijK7Mnxp0ytrCSNdj1OD/lx2RWc0ZhaKSyvL89ktSO9Xlh0KlnijValxHVLqrH7bHox+8WxABbXmHjhKOO94zgO3375LM6PZD/Pk4N+bGi1z1iPVKXIFI4SSRaTkQTtOCoT17ZVI8FysBvUWeeJfNhTOo4iouNofskZfFQtZaMnmih7MTbAO44yJwcWYn79pikUCoVCySAcS4BhpC8uxMlqEk4h3nE0dZFYZdLAE4whnmRx4JIH2wS7NcDvKrc4DOitoOPo2eODePLwAD7yswO4+yf7cOiyB5tpTK0kOpwWXHIFEYplRzAuu4Il9xsRnFYdhn3FOY78kThflru2oeyuCIdRI/bkAFMxoXI7jjqEGOCrwlSlZnt6bPPvb18Og1aJrzx9AkkJl18knsT3d3Xhz37wNsb8uYW3o31eBGNJXBhNd2YM+sJwGDXi7+8LO5bg0fu3oNZ8ZUp6pRbPxGEz4o/AFYil9WyRIm3XZDTtPnO5W4W83izhaAY6jgB+shop0Z3twtGCKgO0KsW0e46O9XmxrsWe1imVj50d9Rj1R3FCcAN6gjG4gzEsqTPxZe4ZbjF3MIb/+eMlPJExSXR8MgpXICrGgecyGpVC/NwAU8fMuSrgzjauWcxfP61ushUlMtoMakyE+c60iOAm100j1jkbsWR0HIViCRgqIBxpJeKYhaDCEYVCoVDmNcFYEga1tM2XCEe9EgXZk5FMx5EWSZbDO90uTEYTWdOLWvMUbZeDwz0eLKw24lt3rcCgL4zJaGLG+zrmC+1OCzgOWTvmoVgCwxORkieqERpsegz5wuA4+cWTfzg+hGji/2/vvuPbLs/9/79uy5b3jEfiOCZ7k5CQhJAQCDO0rFJWC2W0BdoCHaeFzvPrl7bn9HSdlp5CW2YZhVKgFCgdjBJCIHvvvW3HI17ykC1Z9+8PjdiJk3jItqS8n48HDxJb+uiW/IksXbqu9+3jxhnhG1MLykl1cqR9x1EfvWk/Y1AqyQkOVu2rDoU+t5eXnsj3r5zImgO1PLd0X4fvfbizio/9ejEPvbuTLWX1vLLm0AlvZ/HOSgD2HWnssCtMaW1zKJwc/F2G543JPe76Ayn4nLI58Cb9jHaZaHnpgcJRw7GFo+jdzSnUcXRM8SF4DqYl9W3carArdGRear/lz/VUvCOOsQXpbOukkwdgR7mLRxbu4t4X1nDR/77Pdb9bctwbv3q3h+3lri7lGwVdPCEfR5wJFXyDo3Kj8tP8Ye7HFP1qAll/m44ZOw0WvCYMTu/ybUeqxPg4vD4b6sIKbgGf3cMRZulozqhBOOJMt85T8I8D+6z/PI/VUbW0xPhQIDZAQ0tbn+ycm9CDHYBVOBIRkZh2sk9rCrOSccQZDnZaOPJ2GKMIjoT9bX0ZAOceU7QpHuQvHHWnWNBVPp9l1f4aZg3P4dZzh/P+A/N54c5zuHJKYdhv63QwqTAYkN3xDdreUDB27/I5CrOSaGxtO+7N8sm8tOog4wenM+WYTJxwyEn1d8sFBTMiwl2McMQZxg9Jx2f943rOTsI8r502lPPH5vGzt7az/bCLl1Yd5KZHl/KZJ5djreW5z89i1ogcXll16IT/lhbvrMIY8LTZDl1+pbXNFGb231haTwSfUzaW+N9ktx9VS3U6SEqIO75w1By92SqhjKPjRtU8pCfG93luU3BMMdK7jYLGD04/7nnJWsvTH+3lyv/7kJ+/tZ31B2sZkpnE6v01PL98f4fL/mvTYayFGcO7/oY8K8XJuSMH8damw1hr2RkYJR2TH+w46vizCz6XbC6t6zDeFhz/HR8jHUdAKDw4OB4VrZ1/kSY3LZHX7pnLnfNGdOt62YHfWbVNnqMdRzE3qpbQYVStqcVLah9ksyX0IGw7th5pERGRYzS1tnUajA3+T1wKs5I67RQ6Lhw78Enj25sPM7YgLdQdEDR8UCpNrW1UHvOmLxx2VTZQ2+QJvRlIjHcwZ3RuVIblRoKi7GTSE+PZUtbxE/O9vdxRLWhIoHjR1YDsrWX1bDhUx40zhvVJNoi/cNT3o2pwdFztRLkVxhh+fO1kABY89AHffGUDFa4Wvv2x8fzra+czb0we159dxJ6qxlDIb3vVja1sLKnj4vH+XZuC3RHWWkpqmo/rcoo0wcd8c6n/3GtfODLGkJuWSOWxo2qNnj4f6eorRzuOjh1V8/bLLnGThmQytSiT66YX9flthcP4IRlUNbSEzoG6Jg9feG41D/5tC/PG5LLye5fw4bcu4o+fP4e5owfx63/vDGWWVTW08ON/bOXsM7KP+2DjVBZMKmBPVSO7KhrYVdFAcoKDwsxkMpKP7gIYFNzVqrG1rUMe17YyFwUZiT3eWCCSOAPdGMFxtVDHUZR2/kWiM4syu91JE/ywo6aptV3hKLY6jtIDo2rBD04aWrx90nHkVMeRiIhIR40tbZ0GYwcVn2DEzD+qdnzHkavFG9pOtsNxBp04L6m3Vu6rBmDmcGUahYMxJhCQ3fGT/T2V4SkcBcelymq7FpD90qqDJDgMn5g2tFe3eyI5qf4tjINv/o7mdYT/DV4w3+TYfKP2irJT+OWNU7ljznD+8qU5vPeNC/jiBaNCbwA+fuYQkhMcvLL64HHX/XBXFdbCZ+cOBwgFbde7vTS2toVGkyJVsJCys6KB9KT44wpC7XdvBPC0+XC1eKO24yg9FAZ+/KhafxSOMlMSeP2+85hYGB1dMBOG+Me8nvpoL199cS3n/ew9Fm6v4D+vmMATt88IfWBhjOF7H59IXbOHhxfuBODBNzbT1NLGT687s8v5RkGXThwM+PPJdlU0MCo/lbg4Q0ZSfGgXwKD2Rej242pbyupjIt8ICG193hoqHPn/TUbrv8NYEez4qm3y4A78bMK9Tf1AS09KwOuzoVG8xta+CsdW4UhERKSDZo/3hB1H4C8cHTuq1uazNLa2HberWtCx+UbgzzgC+iQge9W+GnLTEjvkoUjvTCz076zW/pP0vVWNFGYm9TqcenReOvFxhuV7q0952RZvG6+tLeGyiYP77JP6nFR/PlcwcLO2uRWnI65PWvyDb9Dbhz535vLJQ3jw6kmd7sCUlhjPx84czJvry2hubevwvcU7KslMTmD2yEEMzUoOdRyVBsLII73jKJjp0+azFOekHHff/YWj4/OoslOjs9Mh3hFHqtNxfMeR2xPaYU6OCu6s9rv3d7N4ZxWXTxrMq1+ay53zRh53rkwszOD66UU8s2Q/T364lzc3lPHli0YzOr/7GUODM5M4a1gWb20uZ1dFA2MCx+gs3DxYRHE64kKFo1avj92VDaH1R7tjR9Xq+mi8V7onWLirbY7djqPg7whXi/+ca2o5cdd8bySq40hERKQjl/vkbb7DclKoamjtGEYYeIPdvnCUleIkzoAxMHvE8YWjouwU4gzsP2Yr7XBYua+amcOjf4vjSDJhSDpNrW0dgtH3VDUyopfB2ODvcjhvTC5/W196XE7Pkl1VbDx09FP6d7dUUNPk4YYZfTdKkxMoOgQDsusD3R59cT5NKszgsokFXDS+d5kyN5w9DFeLl7e3HA59zVrL4p1VnBcY0xyVn8auymMLRwOzg1pXOeIM6YHno84KwXnpzg6Fo2AeVbSOqgGdbule3xy943d9KSfVyR/umMnLXzyXld+7hJ/fMJUzT5J79o3LxuGIM/zozS2MH5zOFy4Y1ePbvnzyYDaW1FFW52Z0vj/nrbNRw+rGVlKdDiYMSWdTIKtrd2UDnjYb6piKds5OOo7i40yfdH5I1wV3tatp9ITGCBNjLOMoWFAPftDT0NJXHUfd//0fW4+0iIjIMcrq3AzJOPGbyTNy/IWC9l1HwRfJ7cOxHXGGnFQnkwozyOzkU0dnfBxDMpM73aGtN8rqmjlU08wMjamF1cQh/jdjwUBXay17KhsY2ctg7KCrphRSUtvM2oNHc3pqGlv53DMrue73S0I7GP151UGGZCYxb0xeWG63Mzmp/vGWYKhtXXPf7dKVGO/gsdtmMHlo70K+zxmRQ1F2Mi+vOrq72q6KBg7Xu5kX2CltdF4auyoa8PlsqHAU6aNqcLSLo7OurNy0RKobW2kLdMLVxsCITGcBy/X9NKoWjS4cn8/M4TldyrAbnJnEPfNHkeAw/Oz6KZ0G0nfVgkmDQ38OFY6Sjx81rGlsJTvVyeShmWwqrcNae3RHtRgZVXM6/B0eLV5/V0tNYGdDfXgzsPwfePifF1titOMovV3hyNvmo8XrO2ncQk9pVzUREZF2WrxtVLpaTjq+EgynPdBJ4Sj9mFGK288dzt3nn/gT3TMGpYR9VG3VvhoAZnZjlxw5tTEFaTjiDFtK/W943tlSjsvtZXyYPjG/dFIBzvg4/ra+NPS155fvx+3xMWJQKl/642oeWbiLxTsruf7soj4NOs8JFB2ChaPapsjv9oiLM1w3vYiPdlexLlB8+2BnFQDnBQpHYwrScHt8lNQ2U1LrJsHhD5eOdMHnleITFI589ujPqiYGRmT8W7p3zDiqP2bXSum5+y4azcrvXcKUoqxeHWdEbipjC/wFo5N2HDW1khMoHLncXg5UN7GtzIUzPo6RvcyHixTBf2+7K/wdxLVNrdpRLQI44gyZyQnUNrfbVS0+tgpHaYn+c8/l9tAYGNVOTQz/fVQ4toiISDvldf6RjyEnGV8Jvnlr33HkCo2qdXxj8+WLx3D11MITHuuMQZ0HbffGqn3VpDgdod2qJDySEhyMyktla1k9pbXNPPDKBiYPzeD6s8MzMpaRlMCF4/L4+4Yy2nyWFm8bzyzdz/lj83j1njnMGZXLz9/ajrX+say+lJPWsXBU1+wJtfxHsptmDiMzOYFPPPIRdz+7ijfWlTAyL5WiQPB28M3trooGSmubGZKZ3O1Q4IEQ7LTprHAUDD8OjqvFQsdRZnJCKBcHwNvmo6HFG+pmkd4xxoStqHHttCIGpTpDmX2ZnWUcNbaSneLkzEBX4caSOraU1TO2II34HrwZjUSzRw5i+KAUHv1gN9ZaappataNahMhKTqCmyYPb4yPOQIIj8p/zu6N9x1FTq/+1aF/sqqZwbBERkXZKujC+kpmSQEZSfIdOoWDhqLtvbIpzUqlubMV1zFhGb6zcV8O04qyYeUEeSSYOyWBTaR1ffXEt3jYfv/n0dBLD+OnlVVMLqXC1sGJvNa+vK6XS1cJd80aQmhjPk3fM4MYZRXx61rDQjnx9JdhxdCSKOo7AH3S96P4L+dolY1i25wjrD9VxfruRvtF5HQtHkZ5vFBTs4jhRxxEQ2o49lHEUxW9ah2Qmh56L4ejzazScg6ebL5w/ksXfujD0++Zox1G7XdUCHUdjCtJIcBg2ldSz7bArZoKxwd/Z8sULRrHhUB0f7qqitsmjjqMIkZXipLbJH46dlOCIufHBYOGowe2lsaXvCkc9CcdWqV9ERGJWWZ3/zcqQzJO/oTxjUGqHTiFXaFSte29shg86urNabzNewD8esO1wPV++aEyvjyXHmzAkg9fWlVJe38KvP3UWI8I8ZnHR+HxSnA7eWF/Kmv01jB+cznmj/WNWifEOfnb91LDe3okkOx0kJzioCRSO6ps9UVOIyExJ4GuXjOVz543gzfVlXDLhaOh2dqqT3DRnqHA0u5PdDiNRRnI8jjjT6QhtcPfGUMdRsz+UNz2KQ3mLc1Jwub3UNfnPu84y5CQyxMWZDnkqwR2eOnYcechOcZIY72Dc4HQW7aik0tUSM/lGQddOH8qv3t3BIwt3Udvk4cyhOl8jQVZKAkcaWnF722Iu3wiOvu6sd3toaPGPqqX1waiaOo5ERETa6eoW3cU5KScYVetmx9Gg4/OSemPtgVp8FmYqGLtPTCr0F/duOLuIa84aGvbjpzjjuWRCAS+vOsj2clenW2r3l5xUJ9WNrXjbfLhavFHX7ZGRlMDN5xSTf0zQ/ai8NLYdrudwvTsqgrEB5o/L58YZRZ2Gk+YeM6oWC6G8w47JkQvmHSkcO/IFdwEMFvtavG00tHhDOzVOLswMbTAwYXBs7KgWlBjv4K55I1m2p5rD9W6yU9VxFAmyU5zUNLXi9vhI6kUYfKQK7qDmcntpCnQcKRxbRESkj5XWuclJdZ7yU6lhOSkcqmkO7WTkOkE49qmcMcjfsbLvSGMPVnu8NftriDNwVnHvQk+lc+eOGsRDN53FD6+Z3Ge3cdXUQrw+S3564knzsfpaTqqT6qbW0MhJtBWOTmRMQRobS+rw2VMXiCPF1VML+Z9PTun0e+mJ8Tjj46hqCORRRclY4ckcuwFBsAgR7ffrdJGRnBAq9gVHJ4NFlPadteNjrOMI4NOzikPZRtEcUB9LslISqG3yh2MnxmDHkSPOkOp04HJ7aQgUjtL6oONU4dgiIiLtdDX3pDgnhdY2H4fr3YD/kx5nfFy3827SEuMZlOrkQJh2Vtt+2MXwQal98qJB/C/QPjFtKMnOvnvxef7YXEbmpnLfRaN7tVV2bwU7juqao3+XrvZG56URqPdGTeHoZIwx5KUlUuUKdhy1RnUwNsCwHP/P5WCN/3kxeA4qHDs6pCcd7TgKBuwHc9OChaOCjERyYrAjJzUxns/OHQFEd0B9LMlKdtLQ4s//SYzBjiPwj6s1tHhoCuyqltIHr1Gc8d3vYtUztoiIxKyyWjdndCF4eFKh/5PSFXuPcO20osBW0T37FVk8KKVD0HZv7Ch3MbYgttr/TzeJ8Q7eu3/+QC+DnFR/FlBwl65Y6fYY0+7fx9AoCcc+ldw0J5UNR8OxoyX0+0TSkxLITkloN6qmjKNo4u848v/MgjlpwY6j8YPTccSZmMs3au/2OcPZWlbP7JHRkaEW67IDY5Ll9S0xmXEE/mJt33ccdf+xi80ynYiICMGOo1N3IZw5NJP89ETe2VIO+EcpuhuMHXRGTkpYMo7cnjb2HWlkbIzlRsjAyEn150IEuz0yk2Pj0/PR+WmhPw/JjP6OI/DvrBYcVattao2J3Zza58hpVC26ZCQlhEZcqwOF52B3UVKCg3vnj+LmWcUDtr6+lpmcwO8+c9EGDnsAACAASURBVHbYN0+Qngk+Hx6ud5OUEJuljLRA4aipNZBx1AeFo4QedBzF5qMtIiKnvXq3B1eLt0uf1sfFGS6dWMD72ytxe9pwub3dzjcKKh6USmldc+gXfk/tqmjAZ2GcOo4kDHJSnTS1tlFR7+9kiZU37fnpiaQnxZOVktAnWxYPhLz0RCpd7cKxY+BnVdSucFTX7MERZ/pk/ELCLyM5/viOo3bFzK9fNo7LJg0ekLXJ6Sf4fFjd2BrDHUcJuNrtqpbSB/dT4dgiIiIBZbX+vKKudiFcNmkwTa1tLNldhcvt6XHhaPbIHKyFLzy3GrenrUfHAP+YGsC4wWmnuKTIqQU7BPZU+YPbYyXjyBjD2IL0qNlRrSty0xKpbmyhubWNZk9bTOzmVJyTQkmtfwOC+mb/KHA07xR3OvF3HAUzjmIrI02iT/uiZVI3cyijRXpSPK5AjlOq00FcXPifK3uSuajCkYiIxKTS2mag64G5s0fmkJYYzztbynG5vT3O35gzKpefXTeFD3dVceczq2hu7VnxaHu5C6cjLrRTm0hvBAtH+wKFo1jpOAL4wdWT+K9P9N3OeP0tN82Jz8LeGCryFeek4GmzHK53U+/2kBFD51+sy0hOoKHFi89nqWlqJSMpvkfdCiLh0P75MFZH1TLajar1xZgaaFc1ERGRkNI6f+Goq50IifEO5o/L450tFdQ197zjCODGmcP4+fVT+Wh3FZ9/ZiWtXl+3j7HjsIuReal6gS5hcbTjqIEUpyOmzqvJQzOZVpw90MsIm9z0RAB2Vvi7DrNiII9qWLZ/k4IDR5qoa/bEVOEy1mUkxWMtuFq8VDe2xuTuaRI9OhaOYrPjKC0xPjSq1le76mpUTUREJKC0tpn4OENe4E1YV1w2aTBVDS1Uulp6HI4ddP3ZRfzwmsks2X2E5XuPdPv6O8obGKdgbAmTUMfRkaaYyMyJZblp/ues3RUNAGTHSMcRwMHqJuqbPdpRLYoEu8Pqmz3UNLXGxOikRK+0xHjiA6NbiT0Yt4oG6UkJuD0+6po9fZYF54gzOLo5Ahebj7aIiJz2ymrdFGQkdesX4/xxeSQ4/JfvTcdR0PljcgE4XOfu1vVcbg8ltc2MVTC2hElOIBei1evTmFCECxaOdgYKR5kxUDgakuV/Lj5Y00S920tGcmwEmZ8OgkW+erfH33EUA7v8SfQyxoR2VovVjqPg68/yOnefbvoQfL3bVSociYhITCqpbe7SjmrtZSQlMHvkIIBedxwB5Kf7b78isENSV+0o979h1I5qEi6ZyQmhImosZObEsrxjCkfZMfBGPcERx5DMJA5Ua1Qt2gSLfPXNXmoa1XEkAy/4OywxRgtHwfG0w/VuUvtw98nu5hypcCQiIjGptK65y8HY7V02sQAIT8dRstNBelJ8aGvtrjq6o5oKRxIecXEmNPKkN+2RLSM5HqcjLhRkHiuFvuKcFA5oVC3qdOg4alLGkQy84O+yWA3HDn5wWdfs6dOOo+7urBabj7aIiJzWfD7L4Tp3jwpHl08ewqTCDKYUZYZlLfnpiVS4ujeqtv2wixSnI6a2GJeBF+xcUeEoshljyE1z4vVZnPFxJMfIp+rFOSnsrmigReOSUSX4fFFR78bt8cVEB5xEt9CoWnxsPDceK6PdB5d9FY4N3e840oCxiIjEnKqGFjxtlsLM7o2qAeSlJ/L3r8wL21ry05Mor+9ex9H2wy7GFKQT183gQpGTCXYKZOmNX8TLTU+ktM5NdkoCxsTG88CwnBTq3V4AFY6iSLDjaN+RJgByUvWzk4EV3OAhdjOOjv4bS3H2YcaROo5EROR0VxoIo+5Jx1G45Wd0v+NoR7mLcQVpfbQiOV0FC0fqOIp8wYDsrOTYKfINC+ysBh0/UZfIlhb4We0/4h+dVMeRDLRgzlasjqqldeg46rviWIIyjkRE5HRXWtsMwJDMgS8cFWQkUVHfgrW2S5evamjhSGOrdlSTsFPhKHrkpgW7w2LnZ1XcvnCkczBqOOIM6Ynx7TqOVDiSgZUZ8x1HRwtHKRpVExER6TvBwlEkZATlpyfS4vVR7/ae8A37//xjK4dqm/nOx8ZzIPDiXMHYEm6DVDiKGqGOoxgqHA3LPvp8rHDs6JKRnMCBav/vJu2qJgMt2PUWqx1H7QtHfRmO3d1RNRWOREQk5pTWuklxOkLbCA+kvHT/G8CKenenb9jbfJY/LttPY2sb722tYOowfyj3OHUcSZhlp8ZeF0usChaOYmksKCfVSarTQWNrm4qXUSY9KZ6SwAcyOTF0Tkp0Cu6qlhij4diJ8Q6c8XG0en2kOvvuPiZqVE1ERE53pbXNFGYlR0SobH66P6C7wtV5QPb2wy4aW9t4YME45o7OZdmearJSEkIFJ5FwGRSDuTmxKjfw7z8zhop8xphQzlEkFPWl64KjhXFGY4Yy8CYVZjIyN5VRebGbBZke6DTq246j7r1G1rO2iIjEnLK65ogIxgYoyAh0HJ0gIHvNgRoArppSyL0XjmbhtgosNiKKXhJbLpmQz4NXTWRSYcZAL0VOIS8GO47AH5C97bBLo2pRJvjzykpx4tBunzLAigel8N798wd6GX0qPSmeI42tpPVl4UgZRyIicrorqW1mwpDIeHOcn+HvOCqv77zjaM3+GnLTnAzL8Re6Lhyf329rk9NLijOeO+aOGOhlSBfkBwrOsTYWNLYgjZX7qmM21DZWBTvEsmOoA04kkqUHirUpfTiqpnBsERE5rR2uc1PVEDm7kqUlxpPidFBxosLRgRqmF2erw0hEQkbmpvLLG6eyYNLggV5KWN0zfzTXnz1soJch3RTsONKOaiL9I9hp1KcdR90Mx1bGkYiIxJT1h2oBmDosa4BXclR+emKno2pVDS3sO9LE9DOyB2BVIhKpjDF8cnpRn+ZbDITUxHhG5KYO9DKkm4K5RrE2OikSqYI7q6X04e8AhWOLiMhpbf3BWuLjTETluORnJHUajr32gL/IdbYKRyIiEqEyAm9i1XEk0j+Co2ppzsjJOFLhSEREYsr6Q7WMH5IeURka+emJVNQf33G0en8N8XGGM4dmDsCqRERETi3UcaTCkUi/ONpx1HevZbu7q5oKRyIiEjN8PsuGg3VMKYqcMTWA/PTOO47W7K9h0tDMiCpyiYiItBfKONKomki/mFacxazhOd3uCuoOp6N7rz1VOBIRkZix90gjrhYvZ0Va4SgjkabWNhpavKGvedp8rD9Uy/TiyFqriIhIe6Fd1dRxJNIvrjlrKC998dw+vQ11HImIyGlr/cHIC8YGKAhsrd1+XG1LaT0tXp/yjUREJKIV56SQ4DCMyU8b6KWISJh0Nxw7trZqEBGR09r6g7WkOB2MjrAXt/npSQCU17cwMs+/tjUHagAFY4uISGQryk5h44MLNFYtEkMUji0iIqeNktrmDn9fd6iOyUMzccR1r/22r+WnBzqOXEc7jlbvr2FIZhJDMpMHalkiIiJdoqKRSGxJiFfhSEQkpllrafPZgV7GgHtp1UHm/uQ9/rzyAAAt3ja2ltZzVoSNqcHRjqPKQEC2tZY1+2uYrm4jEREREelnTnUciYjEtlfXlDD9R+9Q1+QZ6KUMmIp6N//15hYAfvLPbdQ2tbKtzEVrm4+pERaMDf5g0cT4OMoDGUebS+sprXMzb3TuAK9MRERERE436jgSEYlxr6w+RF2zh0U7Kwd6KQPm+69vxu318fvPTKfe7eXnb21nw6FgMHbmAK/ueMYY8jMSqQh0HL25oYz4OMOCSYMHeGUiIiIicrrpbji2CkciIlGkprGVFfuqAfj31vIBXs3A+OfGMv61+TD/cclYLp88hNvPHc4LKw7w0qpD5KY5GZoVmZlB+elJVNS3YK3l7xtLmTs6V1sbi4iIiEi/S4jvXh6oCkciIlHk3a3ltPksE4dk8P72SrxtvoFeUr+qbWrl+29sZlJhBnfNGwHA1y4dw6DURDaW1DGlKAtjIisYOyg/PZEKl5uNJXUcrG7miilDBnpJIiIiInIa0q5qIiIx7K3N5QzNSua+i0ZT1+xh1f6afl9DTWMrLd62fr/dplYvdz27iprGVn563RTiA7/wMpIS+O7HxwNEZL5RUEGGv+PozQ1lJDgMCyZqTE1ERERE+l93w7Hj+2gdIiISZo0tXj7YWcnNs4o5f2weTkcc722rYPbIQf22hoYWL5f+ahE5qU6eumMmRdkp/XK7Ld42vvDcalbvr+HXn5rG5KEdc4yunTYUr89y8fj8fllPT+SlJ+Jq8fLa2hLmjckjMyVhoJckIiIiIqehUflp3bq8Oo5EJOIcrnPj9vR/R0ukW7SjklavjwWTBpOWGM85I3N495icowNHmrDWnvQ4LreHqoaWHq3h2aX7qGpopaSmmWt/u4SNh+p6dJzOrNxXzYc7q45bv6fNx30vrGXxzip+ct0UrppaeNx1jTHcOGMYg9ISw7aecMtP96+twtXCFWdqTE1EREREBsaoPBWORCSK1TV7uPSXi7jpsWU0tXqP+36b7+RFkRPxFx/WMPcn74X++8/XNp6yyBIuGw7VcsPvl3CkhwUbgLc2HyY7JYGZw7MBuHh8PnsqG9lb1QjASysPcv7PF/Lkh3tPepw7n1nFRb94nx3lrm7dflOrlycW72X+uDxeu3cuTkccNz66lIXbK3p2h9qpa/Zwx1Mr+MyTy7n0Vx/w3LL9fLCjku+/vokLfraQd7aU8+BVE7lxxrBe39ZAyc9IAvytwZdOKhjg1YiIiIiIdI0KRyISUV5dcwhXi5cNh2q59/k1eALhz/uqGrn64Q+57ndLenTcn/xzG29uKOOsYVnMHjmIcYPT+eOyAzy+eE84l39CD7+3i5X7avjTigM9un6r18d72yq4dGJBKNvn4gn+4sO/t5azZHcV3/3rRuIMPPrBnhN2bK3YW83yvdU0tbZx+1MrKK1tDn1vT2UDjy7azb82HebAkSZ8xxTpnl92gOrGVr580RjGFKTz13vnMDw3la//eR11zZ4e3a+gP604QGNrGw8sGEdygoP/77VN3PbUCl5adZBJQzP5/Wemc8fcEb26jYFWkOHvODp/bB4ZSRpTExEREZHooIwjEYkY1lqeW7afs4ZlceOMYXz3rxv5zqsbmTcml+/9dRMNLf4OpANHmige1PVsnX9sLOPJD/dyx5zhPHj1pNBt3fP8Gn76r+1MLcrinD7MCTpU08S7W8txxBn+uOwAX7hgVLd3Mli65wgut5cFk44GKg/LSWFcQTovrzpEWV0zI3JT+cZl4/jiH1fzyupDfGb2Gccd55GFu8hNc/LYbTO4/ckV3P7UCh699WyeWbKP55cfwNuuWJSZnMDXLhnD7ecOp7XNx6Mf7OG80bmcfYa/4yk/PYlf3DCFK3/zIb9duIvvfHxCjx6fVq+Ppz/ax5xRg7j3wtHcM38Uaw/WUtfkYfbIQSQ7HT06bqQpzklhRG4qn5ldPNBLERERERHpMnUciciAObZLZenuI+ypbOTW2Wdw8znFfO2SMbyy+hBffXGdv0Po8+cAsGhH10ejdlc28MDL65lWnMV32xU2jDH87PopnJGTwn1/WktFvTs8d6oTzy3bjzGGB6+ayOF6N29vLj/1lY7x4ooDpDodzB2d2+HrF0/IZ3u5iwRHHE/dMZMFkwqYVpzF7xftxhvo1graVFLHoh2VfHbuCKYXZ/PobWez/0gTF/3vIv64/ACfmjWMxd+8kNfuncv/fPJMphRl8oO/beGWJ5bz0Ls7qWpo4SsXj+lwzEmFmXxyWhF/+GgfB6ubQl/3+SzVja1dum9vbijlcL2bu+aNBPw/m+nF2Vw4Pj9mikYAKc54Ft4/n/njIjfAW0RERETkWOo4EpGwe397BW9uKMPl9lDf7GVkXio/uHpSaMQK/MWCL/9pLf95xUQ+f55/BOm5ZfvJSkngiin+4OCvXjwGg8EY+NL8UcTHGYpzUli0o5Jbzx3e6W23eNv45Ts7KKt1U+/2sLWsnsQEB7+9ZTrO+I618vSkBH77mel84pGPuOI3H5KUEEd9s5ecVCfPfX5Wl3cMs9ZijOn0e25PG39eeZDLJhZw8zln8NjiPTyzZF/oPh5r6e4jFA9KYWhWcofH85+bDvONS8eSlNCxkPLJ6UUs3lnFg1dPYliOf733zB/NXc+u4m8bSrl2WlHosr99fxfpifHceq6/E2nOqFweuWU6/9xYxj0XjmJ0fjrg72Q6a1gWn5o5jD+vPMiP3tzC0j1HOGdEDrNG5By35gcWjOPvG0v56b+28fDN0zlc5+YrL65lzf4afvzJM0+aS2St5fHFexmTn8YFY/NOeDkRERERERkYKhyJSFh52nx86y8baGppozArmSSng+eXH2BwRhJfDnSrVDe28v3XN5MQF8eP3txCXnois4bn8PaWcu48b0SoOGKM4auXdOxwmT8uj1dWH6LF20Zi/PHdKAu3VfLooj0UZSeTk+pkbEE6X714DEMyk4+7LMD4wRn87pazeWHFAdIS40lPiufVNSV846X1vHDXbBxxnReEgsrqmvnic6vJSXXym5unk5bY8Wn19XUl1DZ5uH3OcBxxhttmD+e//7GVLaX1TCzMCF3uUE0TD76xhXe3llOQkcjLX5hD8aAUmlvb+P9e38SovFTuvmDkcbc/Oj+Nv335vA5fu3h8PuMK0vnd+7u5ZupQ4uIMuysb+Oemw9wzf1SHfJ1LJxZw6cTOg5qNMXxqVjFzR+fyyMJd3HaCYt3gzCTunjeS/3tvFxOG7OKJxXto8fqYNDSTb76ygYPVTXz90rGdFteW7D7C1rJ6fnrdmcSd4rEWEREREZH+p8KRiITVu1vKKa9v4YnbZnBJoCDx1RfX8tC/dzJvbB5nDcviR29uweX28JcvzeG//r6Vb7y0jvNG5+KzllvOOT6Xp70Lxubx7NL9rNxbw3ljco/7/sJtFaQn+UeCupojdOH4fC4cf3R8aEpRFve/vJ7HF+/hixeMOuH1NpfW8bmnV9Lg9uL2+rjlieU8fcdMslOdgL+b5ukl+xk/OJ1zAp06N84Yxi/f2cEzS/bx0+unUFrbzF/XlvDwe7sAuPfCUTy//AA3P7GMl794Ls8u3c/B6mZevHt2p4WyzsTFGe65cBRffXEddz+3ivSkBLYfdpEYH8dnexAwPSwnhZ9cN+Wkl/nCBaN4YcVBfv7WdiYMyeDhm6dRnJPC9/66kd+8t4uD1U38/Iapx/1MHl+8h9w0J9ecNbTb6xIRERERkb6nwpGI9NjuygaKspM7FDSeXbqfoVnJHQoxP7xmMiv3VvO1F9fywILx/HVtCV+5eAxTirJ4/LYZ3Pj7pSzcXsmF4/JOGXp97qhBOB1xLNpRcVzhyOezvLe9gvPH5HU7fLq966YP5b1t5fzv29s5b3Quk4dmdvi+y+1h0Y5KvvXKBjKSE3jlS3M4VNPMvS+s4cZHl/K/N07F5fay/lAtW8vq+Z9PnhnqtslMSeAT04by6ppDbC6rY1NJPeDvEvrBNZMoyk5hwaTB3Pz4cm56dBmltc3ccHYRs7sZ3n3FmUN4dU0JO8obQl/76sVjyU1L7PHjcjKpifE8dNNZrNpfzRcvGBXqGvvpdVMozknhF2/vICvFGQonB3hl9SHe317JAwvGHTeCJyIiIiIikcFYa099qQEyY8YMu2rVqoFehshprdXrY0e5i/GD00MZRT6f5f/e28lD7+7k0okFPHbr2Rhj2FXh4pJffsA3Lx/HPfNHdzjO0t1HuPmJZQCMzkvjza+cFyo4Ha5z872/buQrF49h6rCsU67p1ieXc7jOzTtfv6DD1zcequOqhz/kFzdM5fqzi05w7a6paWzl8l9/QIoznvnj8nC5vdQ2tbKjvIEDgRDoSYUZPHXHTAoykkL38a5nV4V2fwMYmZvK378yr0PI864KF594ZAljC9K4dOJgLp1YwOj8tA63v3JfNbc+uZzkBAf//sZ8cgJdTNHqR29u4ckP9/Kz66dw44xhrDtYy42PLmXGGdk8+7lZHfKvRERERESkbxljVltrZ3TpsiociciJbDtczzdeWs/m0npG5KZy34WjuWRCAfe/sp53tpQzeWgGm0rq+cHVk7h9znAefGMzLyw/wJLvXNRpZ8tP/7WNxz/Yw0tfPJfpxdk9XtcTi/fwX3/fykffvqhDiPSv393JQ//ewcrvXRKWzpqPdlVxz/Nr8PksGckJpCfFMyovjYmFGUwYks6cUbnHdcrsqWxg7YFaCrOSKcpOZkhmUo+LItsPuwAYNzi91/dloHnbfNzxh5Ws2FvNb26exvdf30SCI4437jsv6otiIiIiIiLRRoUjkRjmbfNR1dBKXnriKYObO7PtcD05KU7yA10ynWlubePpJfv41Ts7yEiO5/PnjeSN9aVsLasnwWHwWfjPKyZw+7nDufPZVXy4s4rn7zqHz/1hJRdPyOehT03r9LjWWmqaPL0uFAQ7m3587ZncfE5x6OvXPPIRBnjt3rm9On57J9sxTbqnprGVax75iAPVTSQnOHj1njlMGJJx6iuKiIiIiEhYdadwpIwjkX5QWttMeb2bab3osgGodLVwyxPL2FHeQHycoTArmRnDs/nxtWd26Hypa/Lw0qqDfPqc4g67fB1paOGahz/C6Yjj/109ieumD8UYQ4u3jTfXl7FwewVby+rZW9WIz8LHJg/mvz4xmUFpiXzxgpG8u7WCl1cd5I65w5kzyp8v9PPrp/Dx/1vMLU8sp9XrC2313hljTFi6S0blpTE0K5n3t1eECkeVrhbWH6zl65eO7fXx21PRKHyyU508ftsM7nthDd+4bKyKRiIiIiIiUUCFI4kJ/dUV4vNZnvpoL099uJdZI3L47NwRp8zkWbmvmrufXUVts4fvfmwCd84bEVrrjnIXTy/Zx/4jjRyqaaau2cMDC8Z1urNYhcvNzY8vp6SmmW9dPp56t4f9Rxp5dU0JTS1tPHLLdBxxhoYWL7f/YQXrDtZS29zKAwvGh47x3LL9tHh9jB+czv0vr+etzYeZMCSDF5bvp6qhlcLMJCYPzeTKKYXMHJ7D3NGDQms1xnS6dfugtEQeumkatzyxjIlDMno1gtZVxhguGJfHa2tL2FnuYkxBOu9vrwDgonah3BJ5xg1OPy6bSkREREREIpdG1STq/e/b2/loVxV//sK5Xd5J62B1E3npid3ayenAkSbuf2U9K/ZWM704ix3lDTS0eJlenMX9l41jzujjt4b/2/pSvvHSeoqykxmdn8bbW8r5zOxivnX5eH77/m4e/2APSQkOxhSkUZSdQnmdmxX7qvnhNZO47dzhoeNU1Lv59OPLKKtz89QdMzvssPXUh3v54ZtbuHX2GXzvignc8YcVrNxXw7iCdPYfaWTxty4iJ9WJ29PGnJ+8x1nD/DuZ/eGjvfzsre142nxcNC6fz84d0aFQ1F2LdlQyNCuJ0fn9k8ezr6qRGx5dirWWF+6azUPv7mD1/hqWfedidQmJiIiIiIichEbVpE+4PW0cqmk+bven/nKwuomslATSkxJCX1t3sJaHF+7CWnh1zSFumll8kiNAi7eNh97dyaOLdjMyL42Hb57G+MH+cRlrLcv3VmOtf8v39j7aVcVdz67CYQy/uGEq100fSkOLl1dWH+LJD/dy8xPLuXbaUL778QnkpDpZd7CGN9aV8szS/cwcns1jt84gMzmBn761jUcX7eGV1Ydwe3zccHYR3wlcB/w7mN37whq+//pm2nyWGWfk8Jc1h3h9XQmtXh9Pf3YWs0bkdFjb584bQbnLzaOL9rBoRyUHa5p46KazmDgkg8se+oDHPtjDtz82nlfXlFDd2Mqd80bgiDPcOW8kV04pxOvzUZSd0uufzwVj83p9jO4YnpvKi3fP5tOPLePTjy3D7WnjqqmFKhqJiIiIiIiEkTqO5JSaW9t4fvl+Hv1gD5WuFn523RRunDms326/ocXLL9/ewdNL9jI6P40/330u2alOvG0+rn74I440tpCblkhtk4eF98/HGd9519Gmkjruf3k92w67uHLKEJbvraa+2cODV0+iKDuZ//v3Tlbuq8ERZ3jithlcGBh52n+kkasf/ojBGUn84bMzKWy3ixf4C2qPLNzF7xftJjnBgTM+jqqGVuLjDJ+cPpQfXjO5Q2fTn1Yc4LW1JXz90rGcM7JjgQr8xaMv/2kNb20uB8DpiOPSSQV86YJRTB6a2el98/ks97+8nlfXlvA/nzyTT8/yF9C+8qe1vLu1nEUPXMhNjy0lOcHBm18+L6aKK3sqG/j048sor2/h0VvPZsGkwQO9JBERERERkYimXdWkx6y1bC1zsam0jpKaZg7VNLNoRwVVDa3MGTUIa2HZ3iP836emcdXUwm4fv67Zw6p91SzfW01ZnZux+f6tzQuzktld2cDWsnr2VDaSk+pkaHYyKQkOHv1gD4fr3Vw5pZC3Nx9mbEE6z991Di+tPMh//X0rv7tlOklOB5/9w8rjdtkC2FvVyMPv7eK1dSUMSnXyk+vO5KLxBVS6Wvj6S+tYvLMKgCGZSdx9/kj+suYQuysaeeGucxhTkM4nf/sRFa4W3rj3PIoHnbgzZ1dFA//79nYccf4soPnj8slMTjjh5U/G0+bjN+/tIj89kaumFJKZcurj+HyWktpmhuUcXeOuigYu+9UizizKYv3BWh666Sw+MW1oj9YUyfYfaeRv60u5+/xRJywcioiIiIiIiF/MFI7OnDrdbli3Oqa6IyLVxkN1/GXNId7ZUk5JbTMAxsDgjCQmFWbwhQtGMXN4Ds2tbdz+1ArWHKjh9585m0uOCUoGfwFjY0kd72wp592t5ZTXu0Pfq2v24LP+Lpq89MTQbQXFxxmG5aRQ29RKTZMHgAlDMvjvayczvTib97aVc/ezq5k8NJMd5S5mjxzEk7f7z/Vrf7uEino3Cx+YT2K8gwNHmvjVuzt4CMFNAAAAEWtJREFUfV0Jzvg4bp51Bl+5eDRZKc4Oa31u2X4SHHFcd/ZQEuMdVLpauO53S3C5PUwemslHu6p49nPncN6Y4zOMosF//Hkdf11bwpDMJD745oVdzoESERERERGR2BQzhaPEIWPsuV9/jEsnFnDxhAKKc1LISE4g1enA02ZxuT243F4KMpJIdnYMOV66+whvrC/lUE0TJTXNtHh93DVvBLfMPqNP3ji73B5W7qtm7uhcEuO7HrjcU942H+WuFo40tFDf7KXe7SE9KZ5ZI3KOu31rLW6Pj3q3hxaPj2E5yR2Kca+vK+EbL63HEWeYNyaXSycWcM6IQRRmJXfaveFye/jME8vZetjFU7fP7FBQKa93c+uTy9lR3kCcgZnDcxhbkE7w5nJSncwakcP04mySEhw0tHjZVlZPaZ2bkbmpjClIC62/scVLhauFYdnJxLf7mf19Qxlf/tMaEuMdvP0f54c6bD7YUcltT63ggQXjqG1q5Zkl+4mLg9vOHc5d80aSl57Y5cd3b1Uj1/9uCUcaW/nPKyZw57yRXb5upNlb1ciChz7g25eP53PnjRjo5YiIiIiIiMgAi5nC0YgJU+y8B55g6e4qPG1H12kMtF92TqqTO+eN4LZzh+P2tPHjv2/l1bUlZCTFMyI3laLsFCobWlixt5oRual86/LxLJhU0KF40tDi5fll+/H6LBMLM5g4JIP89MQTdjtZaznS2Mrm0npeXXOItzYfxu3xccWZQ/jNp6cRF3fyLqmGFi//3FhGSW0zF4zNY2pR1imvU+lq4YdvbmHtgRoO17nx+o7/2aUlxnPB2DwmFmawu6KBLYHRr9Y2X+gys0bk8L2PT2DqsCyeW7qP77+xmZnDc3j81hldGokCqG1q5VOPLWP/kSae+/wsZgzP4UhDCzc9toyy2mb+31WTuHRiAdmpzlMfrAc+2FGJMTBvzNFAZmst1/9+Kav312AM3HB2EV+/dByDM5N6dBs7yl2s2V/DTTOHRX3XW01jK1kpCVF/P0RERERERKT3IrpwZIy5HPg14ACesNb+5ESXDWYcudwelu+ppqqhBZfb312TGB9HRnKCP+x3QxmLdlSSlZKAtdDU6uUL54/ivotGh0KJrbUs3F7Bj/+xjV0VDYzOT+OOOcO55qxC3lhfyq/e2UFVQ2uH23fGx5GRlEBGcjzJCY5Q10xzaxsltc24Pf5iTEZSPFdNLSQ1MZ7HPtjDfReO5v4F4zocy+ezHKhuYktZPe9uKeefmw7T7GkLfT8/PZGzz8jmSEMrh2qaqGv28IlpQ/nS/FEUZafw4c4qvvbndTS0eFgwaTBF2ckUZaeQm5ZIZrJ/jaW1zbyzpZx3tlRQ1dBCQUYiE4ZkMLYgnZxUJxlJCTS0eHjsgz1UNbQya3gOK/ZVc8mEfB6+eXq3tqYHfyHrpkeXUunyhxL/9z+2squigWc+N6vDdvH9aVNJHU8v2cfn5o5gYmHGgKxBREREREREJJJFbOHIGOMAdgCXAoeAlcCnrbVbOrt8d8Kx1x2s5bcLd+Gzlm9/bDyj89M7vZy3zcfr60r5w5K9bCqpJ86Az8LM4dl874qJjMxLZVuZiy2ldZTVu0NjYO7Wo0UeZ3wcQ7OSKcpO5ozcVM4dOYikBAfWWr771438acVBfnHDVD5+5mD+ufEwr60rYc3+GhoDx0hPiufKKYVcf/ZQRuWl8f72St7ecpjNpfUUZCRRlO3ftevN9WVYLHNG5fLBzkpG5aXxyM3TGTe48/sW5PNZXG7vCbuHXG4Pjy7aw+OL93DllEJ+ct2ZPR7fK61t5obfL6WktpkEh+Hx22Ywf1x+j44lIiIiIiIiIn0vkgtH5wIPWmsXBP7+HQBr7f90dvm+3FXNWsvq/TW8uaGM2SNzWDBpcFjGeDxtPu74wwpW7K3G6YijsbWN4pwULhyXFxiBy2Ts4LQu5SCV1jbz+0W7eXHlQa6ZWsgPrplEijO+12sMavG2hSWPaf+RRr75ygY+d94IbYUuIiIiIiIiEuEiuXB0PXC5tfbOwN9vBc6x1t7X2eX7snDUl+qaPXz1xbUUpCdx3dlFzBye3auilLfN1yEcWkRERERERESkp7pTOApf+0rXdFY96VC5MsbcDdwNUFxc3B9rCrvM5ASe/uyssB1PRSMRERERERERGQj9XZE4BAxr9/cioLT9Bay1j1lrZ1hrZ+Tl5SEiIiIiIiIiIgOjvwtHK4ExxpgRxhgn8CngjX5eg4iIiIiIiIiIdEG/jqpZa73GmPuAtwAH8JS1dnN/rkFERERERERERLqmvzOOsNb+A/hHf9+uiIiIiIiIiIh0j1KXRURERERERESkUyociYiIiIiIiIhIp1Q4EhERERERERGRTqlwJCIiIiIiIiIinVLhSEREREREREREOqXCkYiIiIiIiIiIdEqFIxERERERERER6ZQKRyIiIiIiIiIi0ikVjkREREREREREpFMqHImIiIiIiIiISKdUOBIRERERERERkU6pcCQiIiIiIiIiIp1S4UhERERERERERDqlwpGIiIiIiIiIiHRKhSMREREREREREemUCkciIiIiIiIiItIpFY5ERERERERERKRTKhyJiIiIiIiIiEinVDgSEREREREREZFOGWvtQK/hhIwxLmB7uy9lAnUDtJyeiIb15gJVA72IU4iGx1FrDA+dj+GhNYaHzsfw0BrDQ+dj70X6+iA61qhzMTy0xr4RiednNDyO0bDG9qJhvZF4Lh5rkrU2uSsXjO/rlfTSdmvtjOBfjDGPWWvvHsgFdUc0rNcYs6r9YxyJouRx1BrDQOdjeGiN4aHzMTy0xvDQ+dh7kb4+iJo16lwMA62xb0Ti+RkNj2M0rLG9aFhvJJ6LxzLGVHb1stE2qva3gV5AN0XbeiNVNDyOWuPpIxoeR63x9BENj6PWePqI9Mcx0tcH0bHGaBANj6PWePqIhscxGtbYXrStN1LVdvWCkT6qFvFVuminx1giic5HiSQ6HyWS6HyUSKFzUSKZzk+JFNFwLnZnjZHecfTYQC/gNKDHWCKJzkeJJDofJZLofJRIoXNRIpnOT4kU0XAudnmNEd1xJCIiIiIiIiIiAyfSO45ERERERERERGSAqHAUY4wxw4wxC40xW40xm40xXw18PccY844xZmfg/9mBr483xiw1xrQYY+5vd5wkY8wKY8z6wHF+MFD3SaJXuM7HdsdzGGPWGmPe7O/7ItEvnOejMWafMWajMWadMWbVQNwfiW5hPh+zjDGvGGO2BY537kDcJ4leYXz9OC7wvBj8r94Y87WBul8S/cL8XPkfgWNsMsb8yRiTNBD3SaJXmM/HrwbOxc3R8DypUbUYY4wZAgyx1q4xxqQDq4FPAHcA1dbanxhjvg1kW2u/ZYzJB84IXKbGWvuLwHEMkGqtbTDGJAAfAl+11i4bgLslUSpc52O7430dmAFkWGuv7M/7ItEvnOejMWYfMMNaW9Xf90NiQ5jPx2eAxdbaJ4wxTiDFWtvlnVJEwv37OnBMB1ACnGOt3d9f90ViSxjf2wzF/35morW22RjzEvAPa+3T/X+vJFqF8XycDLwIzAJagX8BX7LW7uz3O9VF6jiKMdbaMmvtmsCfXcBWYChwDfBM4GLP4D95sdZWWGtXAp5jjmOttQ2BvyYE/lOVUbolXOcjgDGmCLgCeKIfli4xKJzno0hvhet8NMZkAOcDTwYu16qikXRXHz0/XgzsVtFIeiPM52Y8kGyMiQdSgNI+Xr7EmDCejxOAZdbaJmutF1gEXNsPd6HHVDiKYcaY4cA0YDlQYK0tA/8JD+R34foOY8w6oAJ4x1q7vO9WK7Gut+cj8BDwTcDXR0uU00gYzkcLvG2MWW2Mubuv1imnh16ejyOBSuAPxj/K+4QxJrUPlysxLgzPj0GfAv4U7vXJ6as356a1tgT4BXAAKAPqrLVv9+V6Jbb18rlyE3C+MWaQMSYF+DgwrO9W23sqHMUoY0wa8Bfga9ba+p4cw1rbZq09CygCZgVa6kS6rbfnozHmSqDCWrs67IuT0044nh+Budba6cDHgHuNMeeHbYFyWgnD+RgPTAd+Z62dBjQC3w7jEuU0EqbnRwIjk1cDL4drbXJ6C8NryWz8XSEjgEIg1RjzmfCuUk4XvT0frbVbgZ8C7+AfU1sPeMO6yDBT4SgGBTKJ/gI8b619NfDl8sBMZnA2s6Krxwu0vL8PXB7mpcppIEzn41zg6kCuzIvARcaYP/bRkiWGhev50VpbGvh/BfBX/DPqIt0SpvPxEHCoXVfwK/gLSSLdEubXjx8D1lhry8O/UjndhOncvATYa62ttNZ6gFeBOX21ZoldYXwt+aS1drq19nygGojYfCNQ4SjmBEKtnwS2Wmt/2e5bbwC3B/58O/D6KY6TZ4zJCvw5Gf+T7bbwr1hiWbjOR2vtd6y1Rdba4fhb39+z1upTIumWMD4/pgYCEQmMBF2Gv+VYpMvC+Px4GDhojBkX+NLFwJYwL1diXLjOx3Y+jcbUJAzCeG4eAGYbY1ICx7wYfz6NSJeF87kyEJyNMaYY+CQR/pypXdVijDHmPGAxsJGjWTDfxT97+RJQjP+J8wZrbbUxZjCwCsgIXL4BmAgMxx/s5cBfYHzJWvvD/rsnEgvCdT62bwE1xswH7rfaVU26KYzPj7n4u4zAPyb0grX2v/vrfkhsCOfzozHmLPwbBziBPcBnrbU1/Xl/JLqF+XxMAQ4CI621df17TyTWhPnc/AFwE/6RoLXAndbalv68PxLdwnw+LgYG4Q/O/rq19t/9eme6SYUjERERERERERHplEbVRERERERERESkUyociYiIiIiIiIhIp1Q4EhERERERERGRTqlwJCIiIiIiIiIinVLhSEREREREREREOqXCkYiIiEg3GGOGG2NuPsVlzjTGrAv8V22M2Rv487vGmEJjzCv9tV4RERGR3jDW2oFeg4iIiMiAMMbEW2u93bzOfOB+a+2VXbz808Cb1loVi0RERCTqqONIREREYpox5jZjzAZjzHpjzHPGmKeNMb80xiwEfmqMSTXGPGWMWWmMWWuMuSZwveHGmMXGmDWB/+YEDvkTYF6gg+g/jDEOY8zPA9ffYIz5winWM9wYsynw5zuMMa8ZY/4W6Eq6zxjz9cA6lhljcgKXG2WM+ZcxZnVgTeP78CETERERCYkf6AWIiIiI9BVjzCTge8Bca21VoBDzS2AscIm1ts0Y82PgPWvt54wxWcAKY8y7QAVwqbXWbYwZA/wJmAF8m3YdR8aYu4E6a+1MY0wi8JEx5m1r7d4uLnMyMA1IAnYB37LWTjPG/Aq4DXgIeAz4orV2pzHmHOC3wEW9f4RERERETk6FIxEREYllFwGvWGurAKy11cYYgJettW2By1wGXG2MuT/w9ySgGCgFHjbGnAW04S82deYyYIox5vrA3zOBMUBXC0cLrbUuwGWMqQP+Fvj6xsBx04A5wMuBtQMkdvHYIiIiIr2iwpGIiIjEMgN0FujYeMxlrrPWbu9wRWMeBMqBqfjH+90nuY0vW2vf6uEaW9r92dfu7z78r9XigFpr7Vk9PL6IiIhIjynjSERERGLZv4EbjTGDAIKZQcd4C/iyCbTzGGOmBb6eCZRZa33ArYAj8HUXkH7M9b9kjEkIXH+sMSY1XHfAWlsP7DXG3BA4vjHGTA3X8UVERERORoUjERERiVnW2s3AfwOLjDHr8ecbHetHQAKwIRBa/aPA138L3G6MWYZ/TC3YpbQB8AbCtv8DeALYAqwJXP9Rwt/VfQvw+cB92AxcE+bji4iIiHTKWNtZ97aIiIiIiIiIiJzu1HEkIiIiIiIiIiKdUuFIREREREREREQ6pcKRiIiIiIiIiIh0SoUjERERERERERHplApHIiIiIiIiIiLSKRWORERERERERESkUyociYiIiIiIiIhIp1Q4EhERERERERGRTv3/7QETr87zgLwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "analyzis_data['count'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:lml.utils:failed to import pyecharts_snapshot\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\lml\\utils.py\", line 43, in do_import\n",
      "    plugin_module = __import__(plugin_module_name)\n",
      "ModuleNotFoundError: No module named 'pyecharts_snapshot'\n"
     ]
    }
   ],
   "source": [
    "from pyecharts import Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2012-06-24 00:00:00'),\n",
       " Timestamp('2012-07-01 00:00:00'),\n",
       " Timestamp('2012-07-08 00:00:00'),\n",
       " Timestamp('2012-07-15 00:00:00'),\n",
       " Timestamp('2012-07-22 00:00:00'),\n",
       " Timestamp('2012-07-29 00:00:00'),\n",
       " Timestamp('2012-08-05 00:00:00'),\n",
       " Timestamp('2012-08-12 00:00:00'),\n",
       " Timestamp('2012-08-19 00:00:00'),\n",
       " Timestamp('2012-08-26 00:00:00'),\n",
       " Timestamp('2012-09-02 00:00:00'),\n",
       " Timestamp('2012-09-09 00:00:00'),\n",
       " Timestamp('2012-09-16 00:00:00'),\n",
       " Timestamp('2012-09-23 00:00:00'),\n",
       " Timestamp('2012-09-30 00:00:00'),\n",
       " Timestamp('2012-10-07 00:00:00'),\n",
       " Timestamp('2012-10-14 00:00:00'),\n",
       " Timestamp('2012-10-21 00:00:00'),\n",
       " Timestamp('2012-10-28 00:00:00'),\n",
       " Timestamp('2012-11-04 00:00:00'),\n",
       " Timestamp('2012-11-11 00:00:00'),\n",
       " Timestamp('2012-11-18 00:00:00'),\n",
       " Timestamp('2012-11-25 00:00:00'),\n",
       " Timestamp('2012-12-02 00:00:00'),\n",
       " Timestamp('2012-12-09 00:00:00'),\n",
       " Timestamp('2012-12-16 00:00:00'),\n",
       " Timestamp('2012-12-23 00:00:00'),\n",
       " Timestamp('2012-12-30 00:00:00'),\n",
       " Timestamp('2013-01-06 00:00:00'),\n",
       " Timestamp('2013-01-13 00:00:00'),\n",
       " Timestamp('2013-01-20 00:00:00'),\n",
       " Timestamp('2013-01-27 00:00:00'),\n",
       " Timestamp('2013-02-03 00:00:00'),\n",
       " Timestamp('2013-02-10 00:00:00'),\n",
       " Timestamp('2013-02-17 00:00:00'),\n",
       " Timestamp('2013-02-24 00:00:00'),\n",
       " Timestamp('2013-03-03 00:00:00'),\n",
       " Timestamp('2013-03-10 00:00:00'),\n",
       " Timestamp('2013-03-17 00:00:00'),\n",
       " Timestamp('2013-03-24 00:00:00'),\n",
       " Timestamp('2013-03-31 00:00:00'),\n",
       " Timestamp('2013-04-07 00:00:00'),\n",
       " Timestamp('2013-04-14 00:00:00'),\n",
       " Timestamp('2013-04-21 00:00:00'),\n",
       " Timestamp('2013-04-28 00:00:00'),\n",
       " Timestamp('2013-05-05 00:00:00'),\n",
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       " Timestamp('2013-05-26 00:00:00'),\n",
       " Timestamp('2013-06-02 00:00:00'),\n",
       " Timestamp('2013-06-09 00:00:00'),\n",
       " Timestamp('2013-06-16 00:00:00'),\n",
       " Timestamp('2013-06-23 00:00:00'),\n",
       " Timestamp('2013-06-30 00:00:00'),\n",
       " Timestamp('2013-07-07 00:00:00'),\n",
       " Timestamp('2013-07-14 00:00:00'),\n",
       " Timestamp('2013-07-21 00:00:00'),\n",
       " Timestamp('2013-07-28 00:00:00'),\n",
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       " Timestamp('2013-09-01 00:00:00'),\n",
       " Timestamp('2013-09-08 00:00:00'),\n",
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       " Timestamp('2013-09-29 00:00:00'),\n",
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       " Timestamp('2013-10-13 00:00:00'),\n",
       " Timestamp('2013-10-20 00:00:00'),\n",
       " Timestamp('2013-10-27 00:00:00'),\n",
       " Timestamp('2013-11-03 00:00:00'),\n",
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       " Timestamp('2013-11-24 00:00:00'),\n",
       " Timestamp('2013-12-01 00:00:00'),\n",
       " Timestamp('2013-12-08 00:00:00'),\n",
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       " Timestamp('2013-12-22 00:00:00'),\n",
       " Timestamp('2013-12-29 00:00:00'),\n",
       " Timestamp('2014-01-05 00:00:00'),\n",
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       " Timestamp('2014-01-19 00:00:00'),\n",
       " Timestamp('2014-01-26 00:00:00'),\n",
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       " Timestamp('2014-02-09 00:00:00'),\n",
       " Timestamp('2014-02-16 00:00:00'),\n",
       " Timestamp('2014-02-23 00:00:00'),\n",
       " Timestamp('2014-03-02 00:00:00'),\n",
       " Timestamp('2014-03-09 00:00:00'),\n",
       " Timestamp('2014-03-16 00:00:00'),\n",
       " Timestamp('2014-03-23 00:00:00'),\n",
       " Timestamp('2014-03-30 00:00:00'),\n",
       " Timestamp('2014-04-06 00:00:00'),\n",
       " Timestamp('2014-04-13 00:00:00'),\n",
       " Timestamp('2014-04-20 00:00:00'),\n",
       " Timestamp('2014-04-27 00:00:00'),\n",
       " Timestamp('2014-05-04 00:00:00'),\n",
       " Timestamp('2014-05-11 00:00:00'),\n",
       " Timestamp('2014-05-18 00:00:00'),\n",
       " Timestamp('2014-05-25 00:00:00'),\n",
       " Timestamp('2014-06-01 00:00:00'),\n",
       " Timestamp('2014-06-08 00:00:00'),\n",
       " Timestamp('2014-06-15 00:00:00'),\n",
       " Timestamp('2014-06-22 00:00:00'),\n",
       " Timestamp('2014-06-29 00:00:00'),\n",
       " Timestamp('2014-07-06 00:00:00'),\n",
       " Timestamp('2014-07-13 00:00:00'),\n",
       " Timestamp('2014-07-20 00:00:00'),\n",
       " Timestamp('2014-07-27 00:00:00'),\n",
       " Timestamp('2014-08-03 00:00:00'),\n",
       " Timestamp('2014-08-10 00:00:00'),\n",
       " Timestamp('2014-08-17 00:00:00'),\n",
       " Timestamp('2014-08-24 00:00:00'),\n",
       " Timestamp('2014-08-31 00:00:00'),\n",
       " Timestamp('2014-09-07 00:00:00'),\n",
       " Timestamp('2014-09-14 00:00:00'),\n",
       " Timestamp('2014-09-21 00:00:00'),\n",
       " Timestamp('2014-09-28 00:00:00'),\n",
       " Timestamp('2014-10-05 00:00:00'),\n",
       " Timestamp('2014-10-12 00:00:00'),\n",
       " Timestamp('2014-10-19 00:00:00'),\n",
       " Timestamp('2014-10-26 00:00:00'),\n",
       " Timestamp('2014-11-02 00:00:00'),\n",
       " Timestamp('2014-11-09 00:00:00'),\n",
       " Timestamp('2014-11-16 00:00:00'),\n",
       " Timestamp('2014-11-23 00:00:00'),\n",
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       " Timestamp('2019-01-06 00:00:00'),\n",
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       " Timestamp('2019-01-27 00:00:00'),\n",
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       " Timestamp('2019-02-24 00:00:00'),\n",
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       " Timestamp('2019-03-10 00:00:00'),\n",
       " Timestamp('2019-03-17 00:00:00')]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(analyzis_data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
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       "                \"2016-05-08\",\n",
       "                \"2016-05-15\",\n",
       "                \"2016-05-22\",\n",
       "                \"2016-05-29\",\n",
       "                \"2016-06-05\",\n",
       "                \"2016-06-12\",\n",
       "                \"2016-06-19\",\n",
       "                \"2016-06-26\",\n",
       "                \"2016-07-03\",\n",
       "                \"2016-07-10\",\n",
       "                \"2016-07-17\",\n",
       "                \"2016-07-24\",\n",
       "                \"2016-07-31\",\n",
       "                \"2016-08-07\",\n",
       "                \"2016-08-14\",\n",
       "                \"2016-08-21\",\n",
       "                \"2016-08-28\",\n",
       "                \"2016-09-04\",\n",
       "                \"2016-09-11\",\n",
       "                \"2016-09-18\",\n",
       "                \"2016-09-25\",\n",
       "                \"2016-10-02\",\n",
       "                \"2016-10-09\",\n",
       "                \"2016-10-16\",\n",
       "                \"2016-10-23\",\n",
       "                \"2016-10-30\",\n",
       "                \"2016-11-06\",\n",
       "                \"2016-11-13\",\n",
       "                \"2016-11-20\",\n",
       "                \"2016-11-27\",\n",
       "                \"2016-12-04\",\n",
       "                \"2016-12-11\",\n",
       "                \"2016-12-18\",\n",
       "                \"2016-12-25\",\n",
       "                \"2017-01-01\",\n",
       "                \"2017-01-08\",\n",
       "                \"2017-01-15\",\n",
       "                \"2017-01-22\",\n",
       "                \"2017-01-29\",\n",
       "                \"2017-02-05\",\n",
       "                \"2017-02-12\",\n",
       "                \"2017-02-19\",\n",
       "                \"2017-02-26\",\n",
       "                \"2017-03-05\",\n",
       "                \"2017-03-12\",\n",
       "                \"2017-03-19\",\n",
       "                \"2017-03-26\",\n",
       "                \"2017-04-02\",\n",
       "                \"2017-04-09\",\n",
       "                \"2017-04-16\",\n",
       "                \"2017-04-23\",\n",
       "                \"2017-04-30\",\n",
       "                \"2017-05-07\",\n",
       "                \"2017-05-14\",\n",
       "                \"2017-05-21\",\n",
       "                \"2017-05-28\",\n",
       "                \"2017-06-04\",\n",
       "                \"2017-06-11\",\n",
       "                \"2017-06-18\",\n",
       "                \"2017-06-25\",\n",
       "                \"2017-07-02\",\n",
       "                \"2017-07-09\",\n",
       "                \"2017-07-16\",\n",
       "                \"2017-07-23\",\n",
       "                \"2017-07-30\",\n",
       "                \"2017-08-06\",\n",
       "                \"2017-08-13\",\n",
       "                \"2017-08-20\",\n",
       "                \"2017-08-27\",\n",
       "                \"2017-09-03\",\n",
       "                \"2017-09-10\",\n",
       "                \"2017-09-17\",\n",
       "                \"2017-09-24\",\n",
       "                \"2017-10-01\",\n",
       "                \"2017-10-08\",\n",
       "                \"2017-10-15\",\n",
       "                \"2017-10-22\",\n",
       "                \"2017-10-29\",\n",
       "                \"2017-11-05\",\n",
       "                \"2017-11-12\",\n",
       "                \"2017-11-19\",\n",
       "                \"2017-11-26\",\n",
       "                \"2017-12-03\",\n",
       "                \"2017-12-10\",\n",
       "                \"2017-12-17\",\n",
       "                \"2017-12-24\",\n",
       "                \"2017-12-31\",\n",
       "                \"2018-01-07\",\n",
       "                \"2018-01-14\",\n",
       "                \"2018-01-21\",\n",
       "                \"2018-01-28\",\n",
       "                \"2018-02-04\",\n",
       "                \"2018-02-11\",\n",
       "                \"2018-02-18\",\n",
       "                \"2018-02-25\",\n",
       "                \"2018-03-04\",\n",
       "                \"2018-03-11\",\n",
       "                \"2018-03-18\",\n",
       "                \"2018-03-25\",\n",
       "                \"2018-04-01\",\n",
       "                \"2018-04-08\",\n",
       "                \"2018-04-15\",\n",
       "                \"2018-04-22\",\n",
       "                \"2018-04-29\",\n",
       "                \"2018-05-06\",\n",
       "                \"2018-05-13\",\n",
       "                \"2018-05-20\",\n",
       "                \"2018-05-27\",\n",
       "                \"2018-06-03\",\n",
       "                \"2018-06-10\",\n",
       "                \"2018-06-17\",\n",
       "                \"2018-06-24\",\n",
       "                \"2018-07-01\",\n",
       "                \"2018-07-08\",\n",
       "                \"2018-07-15\",\n",
       "                \"2018-07-22\",\n",
       "                \"2018-07-29\",\n",
       "                \"2018-08-05\",\n",
       "                \"2018-08-12\",\n",
       "                \"2018-08-19\",\n",
       "                \"2018-08-26\",\n",
       "                \"2018-09-02\",\n",
       "                \"2018-09-09\",\n",
       "                \"2018-09-16\",\n",
       "                \"2018-09-23\",\n",
       "                \"2018-09-30\",\n",
       "                \"2018-10-07\",\n",
       "                \"2018-10-14\",\n",
       "                \"2018-10-21\",\n",
       "                \"2018-10-28\",\n",
       "                \"2018-11-04\",\n",
       "                \"2018-11-11\",\n",
       "                \"2018-11-18\",\n",
       "                \"2018-11-25\",\n",
       "                \"2018-12-02\",\n",
       "                \"2018-12-09\",\n",
       "                \"2018-12-16\",\n",
       "                \"2018-12-23\",\n",
       "                \"2018-12-30\",\n",
       "                \"2019-01-06\",\n",
       "                \"2019-01-13\",\n",
       "                \"2019-01-20\",\n",
       "                \"2019-01-27\",\n",
       "                \"2019-02-03\",\n",
       "                \"2019-02-10\",\n",
       "                \"2019-02-17\",\n",
       "                \"2019-02-24\",\n",
       "                \"2019-03-03\",\n",
       "                \"2019-03-10\",\n",
       "                \"2019-03-17\",\n",
       "                \"2019-03-24\",\n",
       "                \"2019-03-31\",\n",
       "                \"2019-04-07\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"nameLocation\": \"middle\",\n",
       "            \"nameGap\": 25,\n",
       "            \"nameTextStyle\": {\n",
       "                \"fontSize\": 14\n",
       "            },\n",
       "            \"axisTick\": {\n",
       "                \"alignWithLabel\": false\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"boundaryGap\": true,\n",
       "            \"type\": \"value\",\n",
       "            \"splitLine\": {\n",
       "                \"show\": true\n",
       "            },\n",
       "            \"axisLine\": {\n",
       "                \"lineStyle\": {\n",
       "                    \"width\": 1\n",
       "                }\n",
       "            },\n",
       "            \"axisLabel\": {\n",
       "                \"interval\": \"auto\",\n",
       "                \"formatter\": \"{value} \",\n",
       "                \"rotate\": 0,\n",
       "                \"margin\": 8,\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 12\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\",\n",
       "        \"#f6f5ec\"\n",
       "    ]\n",
       "};\n",
       "myChart_e7c1a1ad310f4892a457ee1bffa3bed9.setOption(option_e7c1a1ad310f4892a457ee1bffa3bed9);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.line.Line at 0x1b1a411c668>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attr=list(analyzis_data.index)\n",
    "attr_format=[]\n",
    "for item in attr:\n",
    "    attr_format.append(item.strftime('%Y-%m-%d'))\n",
    "v1=list(analyzis_data['count'].values)\n",
    "line = Line()\n",
    "line.add('集思录贴数',attr_format[150:],v1[150:],is_smooth=True,mark_line=[\"max\", \"average\",\"min\"],xaxis_rotate=60)\n",
    "line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 用户统计\n",
    "creator_name_list = []\n",
    "resp_name_list = []\n",
    "\n",
    "for item in doc.find():\n",
    "    creator_name_list.append(item.get('creator'))\n",
    "    for resp in item.get('resp',[]):\n",
    "        x=list(resp.keys())[0]\n",
    "        x=re.sub('_\\d+$','',x)\n",
    "        resp_name_list.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124855"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(creator_name_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1543473"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(resp_name_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "creator_dict={}\n",
    "for i in creator_name_list:\n",
    "    creator_dict.setdefault(i,0)\n",
    "    creator_dict[i]+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "resp_dict={}\n",
    "for i in resp_name_list:\n",
    "    resp_dict.setdefault(i,0)\n",
    "    resp_dict[i]+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = sorted(creator_dict.items(),key=lambda x:x[1],reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_resp = sorted(resp_dict.items(),key=lambda x:x[1],reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16446"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33593"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result_resp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('victor2099', 1),\n",
       " ('poiuytrewq', 1),\n",
       " ('myj68395', 1),\n",
       " ('EXON', 1),\n",
       " ('bonnechiang', 1),\n",
       " ('枫林木', 1),\n",
       " ('gugugua', 1),\n",
       " ('东雷', 1),\n",
       " ('yaya0224', 1),\n",
       " ('ssmsc', 1),\n",
       " ('davidsteve', 1),\n",
       " ('华尔街的小英雄', 1),\n",
       " ('walk_in_sky', 1),\n",
       " ('小佩奇', 1),\n",
       " ('车匪路霸', 1),\n",
       " ('爱cc真是太好了', 1),\n",
       " ('卧云石', 1),\n",
       " ('毛毛大雨', 1),\n",
       " ('嘻哈少年', 1),\n",
       " ('叉二', 1),\n",
       " ('dj36100', 1),\n",
       " ('候补委员长', 1),\n",
       " ('13041041883', 1),\n",
       " ('右冷禅', 1),\n",
       " ('价值新学徒', 1),\n",
       " ('宁静致远666', 1),\n",
       " ('lzqlum', 1),\n",
       " ('15779932518', 1),\n",
       " ('远看一支大葫芦', 1),\n",
       " ('人生巅峰', 1),\n",
       " ('明明就很忙', 1),\n",
       " ('盖世爱', 1),\n",
       " ('夹头扯蛋', 1),\n",
       " ('天亮了2020', 1),\n",
       " ('白色拉法达', 1),\n",
       " ('hsceihgetf', 1),\n",
       " ('john_white', 1),\n",
       " ('pyc', 1),\n",
       " ('森森911', 1),\n",
       " ('tom2017', 1),\n",
       " ('terran2008', 1),\n",
       " ('畅歌', 1),\n",
       " ('liugates', 1),\n",
       " ('pauler', 1),\n",
       " ('jiando', 1),\n",
       " ('取经人', 1),\n",
       " ('乌托邦之夜', 1),\n",
       " ('蜡笔不小心', 1),\n",
       " ('华盛证券', 1),\n",
       " ('江南小舟', 1),\n",
       " ('yangyiking', 1),\n",
       " ('yo0', 1),\n",
       " ('a5452081', 1),\n",
       " ('三十岁学理财', 1),\n",
       " ('Passionsive', 1),\n",
       " ('胡说之', 1),\n",
       " ('sillybird', 1),\n",
       " ('何惹埃', 1),\n",
       " ('赵立刚', 1),\n",
       " ('心中有大山', 1),\n",
       " ('etf500300', 1),\n",
       " ('zw716', 1),\n",
       " ('coco11', 1),\n",
       " ('ojookki', 1),\n",
       " ('有梦想总是好的', 1),\n",
       " ('啊飘啊', 1),\n",
       " ('virind', 1),\n",
       " ('zww2402', 1),\n",
       " ('中国科比', 1),\n",
       " ('fafa', 1),\n",
       " ('fjinhao', 1),\n",
       " ('骆咸阳', 1),\n",
       " ('周华伟', 1),\n",
       " ('xmgign', 1),\n",
       " ('winsgao', 1),\n",
       " ('寻道人', 1),\n",
       " ('谢金昌', 1),\n",
       " ('pp1371', 1),\n",
       " ('ssingh', 1),\n",
       " ('深深升', 1),\n",
       " ('我要放假lnn', 1),\n",
       " ('cjf5736', 1),\n",
       " ('haha00111', 1),\n",
       " ('天下无疾123', 1),\n",
       " ('xk123488', 1),\n",
       " ('勇敢战斗', 1),\n",
       " ('andy12530', 1),\n",
       " ('港卡通', 1),\n",
       " ('0amv', 1),\n",
       " ('吉庆', 1),\n",
       " ('中国雄起', 1),\n",
       " ('seansss', 1),\n",
       " ('bimei5801718', 1),\n",
       " ('小冒号', 1),\n",
       " ('15901713085', 1),\n",
       " ('矿泉水', 1),\n",
       " ('ljzqbasic', 1),\n",
       " ('modeno', 1),\n",
       " ('随风90', 1),\n",
       " ('zhuozhuoqihua', 1)]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[-100:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('东方龙2014_0', 1601),\n",
       " ('东方龙2014_1', 1015),\n",
       " ('luckzpz_3', 774),\n",
       " ('luckzpz_1', 769),\n",
       " ('东方龙2014_2', 753),\n",
       " ('luckzpz_2', 750),\n",
       " ('luckzpz_0', 743),\n",
       " ('luckzpz_4', 717),\n",
       " ('luckzpz_5', 684),\n",
       " ('luckzpz_6', 629),\n",
       " ('DPhil_G_0', 625),\n",
       " ('蔓越橘_0', 577),\n",
       " ('luckzpz_7', 569),\n",
       " ('luckzpz_8', 565),\n",
       " ('东方龙2014_3', 533),\n",
       " ('luckzpz_9', 515),\n",
       " ('蔓越橘_1', 498),\n",
       " ('文明守望_0', 486),\n",
       " ('蔓越橘_2', 473),\n",
       " ('luckzpz_10', 462)]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_resp[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'get'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-98-4145b10ded8f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'luckzpz_10'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'get'"
     ]
    }
   ],
   "source": [
    "result.get('luckzpz_10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_resp_dict=dict(result_resp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_dict=dict(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_dict.get('量化自由之路')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "165"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_resp_dict.get('量化自由之路')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "409073"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result_resp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('luckzpz', 16199),\n",
       " ('东方龙2014', 7624),\n",
       " ('neo牛3', 7406),\n",
       " ('蔓越橘', 7273),\n",
       " ('文明守望', 6742)]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_resp[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('帅牛', 2677),\n",
       " ('英为财情', 1600),\n",
       " ('luckzpz', 1233),\n",
       " ('orchimike', 1070),\n",
       " ('西楼', 1026)]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'东方龙2014_0': 1601,\n",
       " '东方龙2014_1': 1015,\n",
       " 'luckzpz_3': 774,\n",
       " 'luckzpz_1': 769,\n",
       " '东方龙2014_2': 753,\n",
       " 'luckzpz_2': 750,\n",
       " 'luckzpz_0': 743,\n",
       " 'luckzpz_4': 717,\n",
       " 'luckzpz_5': 684,\n",
       " 'luckzpz_6': 629,\n",
       " 'DPhil_G_0': 625,\n",
       " '蔓越橘_0': 577,\n",
       " 'luckzpz_7': 569,\n",
       " 'luckzpz_8': 565,\n",
       " '东方龙2014_3': 533,\n",
       " 'luckzpz_9': 515,\n",
       " '蔓越橘_1': 498,\n",
       " '文明守望_0': 486,\n",
       " '蔓越橘_2': 473,\n",
       " 'luckzpz_10': 462,\n",
       " '文明守望_1': 441,\n",
       " '天书_1': 440,\n",
       " '东方龙2014_4': 439,\n",
       " 'luckzpz_11': 439,\n",
       " 'DPhil_G_1': 432,\n",
       " 'neo牛3_0': 427,\n",
       " '文明守望_2': 426,\n",
       " 'neo牛3_1': 414,\n",
       " '东方龙2014_5': 406,\n",
       " '文明守望_3': 405,\n",
       " '李季峰_0': 391,\n",
       " 'neo牛3_2': 387,\n",
       " 'neo牛3_3': 378,\n",
       " '蔓越橘_3': 374,\n",
       " 'luckzpz_12': 373,\n",
       " '文明守望_4': 373,\n",
       " 'luckzpz_13': 361,\n",
       " '天书_3': 357,\n",
       " '天书_2': 354,\n",
       " 'neo牛3_4': 351,\n",
       " '天书_0': 350,\n",
       " '量化投资先锋_0': 347,\n",
       " 'luckzpz_15': 344,\n",
       " '文明守望_5': 342,\n",
       " '伏尔泰_0': 342,\n",
       " 'Baoman1971_0': 336,\n",
       " 'neo牛3_5': 327,\n",
       " 'luckzpz_17': 326,\n",
       " '西楼_0': 326,\n",
       " 'luckzpz_14': 325,\n",
       " '东方龙2014_6': 321,\n",
       " '蔓越橘_5': 321,\n",
       " '量化投资先锋_1': 321,\n",
       " 'ylshxajh_1': 318,\n",
       " 'DPhil_G_2': 318,\n",
       " 'luckzpz_16': 314,\n",
       " 'xcgdgp_0': 312,\n",
       " '天书_4': 309,\n",
       " '量化投资先锋_2': 307,\n",
       " '蔓越橘_4': 306,\n",
       " '蔓越橘_6': 306,\n",
       " 'ylshxajh_2': 298,\n",
       " 'neo牛3_6': 297,\n",
       " 'Baoman1971_1': 295,\n",
       " '天书_5': 292,\n",
       " 'luckzpz_19': 290,\n",
       " 'neo牛3_7': 288,\n",
       " '文明守望_6': 284,\n",
       " 'ylshxajh_0': 278,\n",
       " 'ylshxajh_3': 277,\n",
       " 'luckzpz_18': 273,\n",
       " '与时间为友_1': 271,\n",
       " '伏尔泰_1': 271,\n",
       " 'luckzpz_22': 269,\n",
       " 'ylshxajh_4': 265,\n",
       " 'xcgdgp_1': 264,\n",
       " '花园小琴_1': 262,\n",
       " 'luckzpz_20': 261,\n",
       " '量化投资先锋_3': 261,\n",
       " '西楼_1': 259,\n",
       " '梦想会计师_1': 257,\n",
       " '伏尔泰_2': 255,\n",
       " '帅牛_2': 254,\n",
       " 'DPhil_G_3': 254,\n",
       " '花园小琴_0': 254,\n",
       " '东方龙2014_7': 251,\n",
       " 'lwhgjy_0': 251,\n",
       " 'neo牛3_8': 251,\n",
       " 'luckzpz_21': 250,\n",
       " '李季峰_1': 249,\n",
       " 'rypan_0': 248,\n",
       " 'lailai2000_0': 248,\n",
       " '天书_6': 245,\n",
       " '抄顶逃底_0': 242,\n",
       " 'neo牛3_9': 242,\n",
       " 'rypan_1': 239,\n",
       " '蔓越橘_8': 239,\n",
       " '西胖子_2': 239,\n",
       " 'ylshxajh_7': 238,\n",
       " '酱油面_1': 237,\n",
       " 'ylshxajh_5': 237,\n",
       " '健身教练去吸脂_0': 237,\n",
       " 'lwhgjy_1': 237,\n",
       " '蔓越橘_7': 236,\n",
       " '量化投资先锋_4': 236,\n",
       " '帅牛_0': 236,\n",
       " '梦想会计师_0': 236,\n",
       " '与时间为友_2': 235,\n",
       " '伏尔泰_3': 235,\n",
       " '蔓越橘_9': 234,\n",
       " 'freeman1980_0': 233,\n",
       " 'starhill_0': 233,\n",
       " '与时间为友_3': 232,\n",
       " '与时间为友_0': 232,\n",
       " '西楼_2': 230,\n",
       " 'xineric_0': 230,\n",
       " '东方龙2014_8': 229,\n",
       " 'ylshxajh_8': 226,\n",
       " '帅牛_1': 226,\n",
       " 'lzw1969_0': 225,\n",
       " '西胖子_1': 224,\n",
       " '量化投资先锋_5': 224,\n",
       " '转债痴迷者_0': 223,\n",
       " 'lailai2000_1': 223,\n",
       " '梦想会计师_2': 222,\n",
       " '量化投资先锋_6': 222,\n",
       " 'neo牛3_10': 222,\n",
       " 'rotation_2': 221,\n",
       " 'DPhil_G_4': 221,\n",
       " '天书_7': 220,\n",
       " 'rypan_2': 219,\n",
       " 'ylshxajh_6': 219,\n",
       " '酱油面_0': 218,\n",
       " '文明守望_7': 218,\n",
       " '帅牛_3': 217,\n",
       " '西胖子_3': 216,\n",
       " 'xcgdgp_2': 216,\n",
       " '胆小_0': 216,\n",
       " 'neo牛3_12': 215,\n",
       " '穿风_1': 215,\n",
       " '文明守望_8': 215,\n",
       " '忧蓝_0': 215,\n",
       " '穿风_0': 214,\n",
       " '守心斋_0': 212,\n",
       " 'shine888_0': 211,\n",
       " '帅牛_4': 210,\n",
       " '文明守望_9': 210,\n",
       " '花园小琴_2': 210,\n",
       " '酱油面_2': 210,\n",
       " '东方龙2014_9': 209,\n",
       " 'coolie_1': 209,\n",
       " 'shine888_1': 209,\n",
       " 'neo牛3_11': 209,\n",
       " '天书_8': 208,\n",
       " 'blacklevi0823_0': 208,\n",
       " 'xineric_1': 207,\n",
       " 'luckzpz_23': 205,\n",
       " 'blacklevi0823_1': 204,\n",
       " '文明守望_10': 204,\n",
       " 'rotation_0': 204,\n",
       " 'lailai2000_2': 203,\n",
       " 'DPhil_G_5': 202,\n",
       " '李季峰_2': 202,\n",
       " '抄顶逃底_1': 202,\n",
       " 'yugong1994_0': 201,\n",
       " 'luckzpz_26': 201,\n",
       " '穿风_2': 199,\n",
       " '西胖子_0': 199,\n",
       " '花园小琴_4': 197,\n",
       " 'jk1968_0': 197,\n",
       " '与时间为友_4': 197,\n",
       " 'rypan_3': 196,\n",
       " '西楼_4': 196,\n",
       " 'xcgdgp_3': 195,\n",
       " '没钱又丑_0': 194,\n",
       " '蔓越橘_11': 194,\n",
       " '文明守望_11': 194,\n",
       " 'Baoman1971_2': 194,\n",
       " '牛村老叟_0': 194,\n",
       " '羽痕_0': 194,\n",
       " '蔓越橘_10': 193,\n",
       " '伏尔泰_5': 193,\n",
       " 'beatcpi_0': 193,\n",
       " 'luckzpz_24': 193,\n",
       " '西楼_3': 192,\n",
       " '天书_9': 191,\n",
       " '转债痴迷者_1': 191,\n",
       " '西胖子_4': 191,\n",
       " '低姿态_5': 190,\n",
       " 'ylshxajh_9': 190,\n",
       " 'sUlla_0': 190,\n",
       " 'lzw1969_2': 190,\n",
       " 'mysteed_1': 190,\n",
       " '伏尔泰_4': 190,\n",
       " 'lzw1969_1': 189,\n",
       " 'orchimike_0': 189,\n",
       " '羽痕_1': 189,\n",
       " '量化投资先锋_7': 189,\n",
       " 'rotation_1': 187,\n",
       " 'lwhgjy_2': 187,\n",
       " 'blacklevi0823_2': 186,\n",
       " '西胖子_5': 186,\n",
       " 'jiangdaya_1': 185,\n",
       " '守心斋_2': 185,\n",
       " 'neo牛3_13': 184,\n",
       " '花园小琴_3': 183,\n",
       " '没钱又丑_1': 183,\n",
       " 'coolie_2': 183,\n",
       " 'shine888_2': 183,\n",
       " '守心斋_1': 182,\n",
       " 'freeman1980_2': 181,\n",
       " '李季峰_3': 181,\n",
       " 'ylshxajh_10': 181,\n",
       " 'cile_1': 180,\n",
       " 'tintin_0': 179,\n",
       " '忧蓝_1': 179,\n",
       " '健身教练去吸脂_1': 179,\n",
       " 'jiangdaya_0': 179,\n",
       " 'cjplove_0': 178,\n",
       " '转债痴迷者_2': 178,\n",
       " 'neo牛3_14': 178,\n",
       " '天书_11': 177,\n",
       " 'lzw1969_3': 177,\n",
       " '帅牛_5': 176,\n",
       " 'rotation_5': 176,\n",
       " '牛村村民_0': 175,\n",
       " '忧蓝_2': 175,\n",
       " 'cile_0': 174,\n",
       " 'yugong1994_2': 174,\n",
       " 'mysteed_0': 174,\n",
       " 'rypan_4': 174,\n",
       " '梦想会计师_3': 174,\n",
       " 'coolie_0': 174,\n",
       " 'mysteed_2': 173,\n",
       " '低姿态_2': 173,\n",
       " '牛村老叟_1': 173,\n",
       " 'neo牛3_15': 173,\n",
       " '低姿态_6': 172,\n",
       " '与时间为友_6': 172,\n",
       " 'coolie_3': 172,\n",
       " 'freeman1980_1': 172,\n",
       " 'xineric_2': 171,\n",
       " 'yiyayi_0': 171,\n",
       " 'vittata_1': 171,\n",
       " '西楼_5': 171,\n",
       " 'starhill_1': 171,\n",
       " '临江之麋_2': 170,\n",
       " '低姿态_3': 170,\n",
       " '伏尔泰_7': 170,\n",
       " '与时间为友_8': 170,\n",
       " '临江之麋_1': 169,\n",
       " 'ylshxajh_11': 169,\n",
       " 'jiangdaya_2': 169,\n",
       " 'DPhil_G_7': 169,\n",
       " 'blacklevi0823_3': 168,\n",
       " '穿风_3': 168,\n",
       " 'xcgdgp_4': 168,\n",
       " 'vittata_4': 168,\n",
       " 'jk1968_2': 168,\n",
       " 'lailai2000_4': 168,\n",
       " '量化投资先锋_11': 168,\n",
       " '花园小琴_5': 167,\n",
       " '债券小白_0': 167,\n",
       " '伏尔泰_6': 167,\n",
       " '梦想会计师_4': 167,\n",
       " 'jk1968_1': 167,\n",
       " '15260527807_0': 167,\n",
       " '天书_10': 166,\n",
       " '文明守望_12': 166,\n",
       " '蔓越橘_12': 166,\n",
       " 'shine888_3': 166,\n",
       " '西楼_6': 166,\n",
       " '牛村村民_1': 166,\n",
       " '量化投资先锋_8': 166,\n",
       " 'rotation_3': 166,\n",
       " '胆小_2': 165,\n",
       " 'xineric_3': 165,\n",
       " 'vittata_0': 165,\n",
       " '西胖子_7': 165,\n",
       " '牛村老叟_2': 165,\n",
       " '守心斋_3': 165,\n",
       " '守心斋_4': 165,\n",
       " 'jack19921_0': 165,\n",
       " '酱油面_4': 164,\n",
       " 'yugong1994_4': 164,\n",
       " '评级上调_1': 164,\n",
       " '量化投资先锋_9': 164,\n",
       " '评级上调_0': 163,\n",
       " 'lzw1969_4': 163,\n",
       " 'yugong1994_1': 162,\n",
       " 'shuijing_0': 162,\n",
       " '低姿态_8': 162,\n",
       " 'Baoman1971_3': 162,\n",
       " '没钱又丑_2': 161,\n",
       " '东方龙2014_10': 161,\n",
       " 'beatcpi_1': 161,\n",
       " '没胆又穷_0': 160,\n",
       " '健身教练去吸脂_2': 160,\n",
       " '没胆又穷_1': 160,\n",
       " 'lwhgjy_3': 160,\n",
       " 'lailai2000_3': 160,\n",
       " 'starhill_2': 160,\n",
       " 'freeman1980_4': 160,\n",
       " 'luckzpz_28': 159,\n",
       " '低姿态_9': 159,\n",
       " 'cjplove_1': 159,\n",
       " 'luckzpz_25': 159,\n",
       " '割总_0': 159,\n",
       " 'lwhgjy_4': 159,\n",
       " 'blacklevi0823_4': 158,\n",
       " '低姿态_4': 158,\n",
       " 'ylshxajh_12': 158,\n",
       " 'mysteed_3': 157,\n",
       " 'ylshxajh_13': 157,\n",
       " 'rypan_5': 157,\n",
       " '小竹_3': 157,\n",
       " '林奇_1': 157,\n",
       " '紫葳侍郎_1': 157,\n",
       " '牛死了_1': 157,\n",
       " '债券小白_1': 156,\n",
       " '抄顶逃底_2': 156,\n",
       " 'DPhil_G_6': 156,\n",
       " 'luckzpz_29': 155,\n",
       " '临江之麋_0': 155,\n",
       " 'vittata_2': 155,\n",
       " '波段分级_0': 155,\n",
       " 'tintin_1': 154,\n",
       " 'rotation_7': 154,\n",
       " 'rotation_8': 154,\n",
       " '抄顶逃底_3': 154,\n",
       " 'jiangdaya_3': 154,\n",
       " '拌拌_0': 153,\n",
       " 'luckzpz_27': 153,\n",
       " '酱油面_3': 153,\n",
       " '低姿态_1': 153,\n",
       " 'vittata_3': 153,\n",
       " 'rotation_4': 153,\n",
       " 'rotation_6': 153,\n",
       " '胆小_1': 153,\n",
       " 'wlqzjr_0': 153,\n",
       " '蔓越橘_13': 152,\n",
       " '割总_1': 152,\n",
       " 'lzw1969_5': 152,\n",
       " '石诺_0': 152,\n",
       " 'neo牛3_17': 152,\n",
       " 'jiangdaya_4': 152,\n",
       " 'xuyongnn_3': 150,\n",
       " 'neo牛3_16': 150,\n",
       " 'vittata_6': 150,\n",
       " 'yugong1994_3': 150,\n",
       " '量化投资先锋_10': 150,\n",
       " 'neo牛3_18': 149,\n",
       " 'yiyayi_1': 149,\n",
       " 'vittata_8': 149,\n",
       " '与时间为友_5': 149,\n",
       " '羽痕_4': 149,\n",
       " '文明守望_14': 149,\n",
       " 'shuijing_1': 149,\n",
       " '牛村村民_3': 149,\n",
       " 'lailai2000_6': 149,\n",
       " 'cile_2': 148,\n",
       " 'blacklevi0823_5': 148,\n",
       " 'wlqzjr_1': 148,\n",
       " '6019233124_0': 148,\n",
       " 's52510447_0': 148,\n",
       " '小竹_1': 147,\n",
       " '梦想会计师_5': 147,\n",
       " 'JYDD2003_1': 147,\n",
       " '伏尔泰_8': 147,\n",
       " '羽痕_3': 147,\n",
       " 'shine888_4': 147,\n",
       " '牛死了_2': 147,\n",
       " 'JYDD2003_3': 147,\n",
       " '太原业余基民_0': 146,\n",
       " '低姿态_7': 146,\n",
       " '花园小琴_6': 146,\n",
       " 'beatcpi_2': 146,\n",
       " 'shuifeng2009_0': 146,\n",
       " '牛村老叟_4': 146,\n",
       " '忧蓝_4': 146,\n",
       " 'xuyongnn_1': 146,\n",
       " '穿风_4': 145,\n",
       " '东方龙2014_11': 145,\n",
       " '帅牛_6': 145,\n",
       " '拌拌_1': 144,\n",
       " '天书_13': 144,\n",
       " 'xwy203333_1': 144,\n",
       " '健身教练去吸脂_3': 144,\n",
       " 'orchimike_1': 144,\n",
       " '忧蓝_5': 144,\n",
       " '酱油面_6': 143,\n",
       " 'luckzpz_30': 143,\n",
       " 'luckzpz_31': 143,\n",
       " 'suliang_0': 143,\n",
       " 'ylshxajh_15': 143,\n",
       " '天书_12': 143,\n",
       " '西胖子_9': 143,\n",
       " '忧蓝_3': 143,\n",
       " 'Baoman1971_4': 143,\n",
       " '帅牛_11': 142,\n",
       " 'yiyayi_2': 142,\n",
       " '低姿态_10': 142,\n",
       " '常存_1': 142,\n",
       " '天书_17': 142,\n",
       " 'starhill_3': 142,\n",
       " '小竹_4': 141,\n",
       " 'mysteed_4': 141,\n",
       " '量化投资先锋_12': 141,\n",
       " 'xuyongnn_2': 140,\n",
       " '帅牛_9': 140,\n",
       " 'xwy203333_0': 140,\n",
       " '水无常形_1': 140,\n",
       " '太原业余基民_1': 140,\n",
       " 'shine888_5': 140,\n",
       " 'lailai2000_5': 140,\n",
       " 'freeman1980_3': 140,\n",
       " '小竹_2': 139,\n",
       " '常存_0': 139,\n",
       " 'vittata_5': 139,\n",
       " '酱油面_5': 139,\n",
       " 'zhaojinze778_1': 139,\n",
       " 'starhill_4': 139,\n",
       " 'cmx_0': 139,\n",
       " 'urfatu_0': 138,\n",
       " 'coolie_4': 138,\n",
       " 'xcgdgp_6': 138,\n",
       " '如_佛_4': 137,\n",
       " '文明守望_13': 137,\n",
       " '天书_14': 137,\n",
       " '帅牛_7': 137,\n",
       " 'zhaojinze778_0': 137,\n",
       " '6019233124_2': 137,\n",
       " '西胖子_8': 137,\n",
       " '牛村老叟_3': 137,\n",
       " 'shine888_7': 137,\n",
       " 'JYDD2003_2': 137,\n",
       " 'a167387_0': 136,\n",
       " '酱油面_7': 136,\n",
       " '帅牛_8': 136,\n",
       " '与时间为友_7': 136,\n",
       " 'viking_75_0': 136,\n",
       " '毛之川_1': 136,\n",
       " 'shuijing_3': 136,\n",
       " '梦想会计师_7': 136,\n",
       " 'freeman1980_6': 136,\n",
       " 'jack19921_3': 136,\n",
       " 'DPhil_G_8': 136,\n",
       " '李季峰_4': 135,\n",
       " '低姿态_14': 135,\n",
       " '评级上调_3': 135,\n",
       " '没胆又穷_2': 135,\n",
       " '林奇_0': 135,\n",
       " 'cile_3': 134,\n",
       " '毛之川_0': 134,\n",
       " 'yiyayi_5': 134,\n",
       " 'shuijing_2': 134,\n",
       " '6019233124_1': 134,\n",
       " '转债痴迷者_4': 134,\n",
       " '低姿态_12': 133,\n",
       " '低姿态_13': 133,\n",
       " 'sUlla_1': 133,\n",
       " '转债痴迷者_3': 133,\n",
       " '傻祥_0': 133,\n",
       " 'yiyayi_3': 132,\n",
       " '西楼_7': 132,\n",
       " '羽痕_5': 132,\n",
       " '牛村村民_2': 132,\n",
       " 'yh123556_0': 132,\n",
       " '现金A_0': 132,\n",
       " 'shysky123_0': 132,\n",
       " '花园小琴_8': 132,\n",
       " '林奇_3': 131,\n",
       " 'a167387_1': 131,\n",
       " 'xcgdgp_5': 131,\n",
       " '抄顶逃底_4': 131,\n",
       " '紫葳侍郎_2': 131,\n",
       " 'vittata_7': 131,\n",
       " 'xcgdgp_7': 131,\n",
       " '守心斋_5': 131,\n",
       " 'jack19921_1': 131,\n",
       " 'xuyongnn_0': 130,\n",
       " '蔓越橘_14': 130,\n",
       " '忧蓝_6': 130,\n",
       " '梦想会计师_6': 130,\n",
       " '紫葳侍郎_0': 130,\n",
       " 'sUlla_2': 130,\n",
       " '健身教练去吸脂_4': 130,\n",
       " 'lzw1969_6': 130,\n",
       " '牛死了_0': 130,\n",
       " 'gypeng123_0': 130,\n",
       " '常存_3': 130,\n",
       " 'neo牛3_21': 130,\n",
       " '临江之麋_5': 129,\n",
       " '伏尔泰_9': 129,\n",
       " '低姿态_15': 129,\n",
       " 'suliang_1': 129,\n",
       " '温酒斩华佗_0': 129,\n",
       " 'wss115_0': 129,\n",
       " '低姿态_0': 129,\n",
       " 'zyc641124_4': 129,\n",
       " 'coolie_5': 129,\n",
       " 'DPhil_G_9': 129,\n",
       " '没钱又丑_3': 128,\n",
       " '东方龙2014_12': 128,\n",
       " 'luckzpz_32': 128,\n",
       " '与时间为友_9': 128,\n",
       " '羽痕_2': 128,\n",
       " '与时间为友_10': 128,\n",
       " 'rypan_6': 128,\n",
       " 'sUlla_3': 128,\n",
       " '西胖子_6': 128,\n",
       " '低姿态_11': 128,\n",
       " 'yugong1994_5': 128,\n",
       " '常存_4': 128,\n",
       " '蔓越橘_15': 127,\n",
       " 'beatcpi_3': 127,\n",
       " 'rotation_11': 127,\n",
       " 'chris1234_0': 127,\n",
       " 'rotation_9': 127,\n",
       " '量化投资先锋_13': 127,\n",
       " '波段分级_1': 127,\n",
       " 'jack19921_2': 127,\n",
       " 'blacklevi0823_7': 126,\n",
       " '林奇_2': 126,\n",
       " '临江之麋_4': 126,\n",
       " '牛死了_5': 126,\n",
       " '量化投资先锋_14': 126,\n",
       " 'silizhe168_1': 126,\n",
       " '小竹_0': 125,\n",
       " 'wlqzjr_2': 125,\n",
       " '学习再学习_1': 125,\n",
       " 'neo牛3_19': 125,\n",
       " 'ylshxajh_16': 125,\n",
       " '牛明雷_2': 125,\n",
       " '石诺_1': 125,\n",
       " '伏尔泰_10': 125,\n",
       " 'xineric_4': 124,\n",
       " '没钱又丑_5': 124,\n",
       " 'gzyy_0': 124,\n",
       " 'silizhe168_0': 124,\n",
       " '文明守望_16': 124,\n",
       " 'vittata_9': 124,\n",
       " 'ylshxajh_14': 124,\n",
       " '文明守望_15': 124,\n",
       " '我是刚来的_0': 124,\n",
       " 'jiangdaya_6': 124,\n",
       " '抄顶逃底_5': 123,\n",
       " 'hello123_0': 123,\n",
       " 'jsfq_1': 123,\n",
       " '东方龙2014_13': 123,\n",
       " 'jsfq_0': 123,\n",
       " '临江之麋_3': 123,\n",
       " 'chh919_1': 123,\n",
       " '如_佛_0': 123,\n",
       " 'jiangdaya_8': 123,\n",
       " 'yiyayi_4': 123,\n",
       " 'shine888_6': 123,\n",
       " 'lwhgjy_5': 123,\n",
       " 'starhill_5': 123,\n",
       " 'xcgdgp_8': 123,\n",
       " 'viking_75_1': 123,\n",
       " '胆小_3': 123,\n",
       " 'cile_4': 122,\n",
       " 'wxy466_1': 122,\n",
       " '蔓越橘_16': 122,\n",
       " 'xwy203333_2': 122,\n",
       " 'cjplove_2': 122,\n",
       " 'urfatu_1': 122,\n",
       " 'zyc641124_3': 122,\n",
       " 'lailai2000_7': 122,\n",
       " 'jiangdaya_5': 122,\n",
       " 'xvz_1': 121,\n",
       " '水无常形_3': 121,\n",
       " 'xineric_5': 121,\n",
       " '现金A_1': 121,\n",
       " 'JYDD2003_0': 121,\n",
       " '学习再学习_0': 121,\n",
       " 'luckzpz_34': 121,\n",
       " '林奇_4': 121,\n",
       " '牛村老叟_5': 121,\n",
       " 'blacklevi0823_6': 121,\n",
       " 'lzw1969_9': 121,\n",
       " 'yh123556_3': 121,\n",
       " 'jk1968_3': 121,\n",
       " '常存_2': 121,\n",
       " '守心斋_6': 121,\n",
       " '花园小琴_7': 120,\n",
       " '低姿态_18': 120,\n",
       " '毛之川_2': 120,\n",
       " '水无常形_2': 120,\n",
       " '没胆又穷_3': 120,\n",
       " 'cjplove_3': 120,\n",
       " '新人路过_1': 120,\n",
       " '花园小琴_9': 119,\n",
       " '如_佛_2': 119,\n",
       " '西胖子_10': 119,\n",
       " 'rypan_7': 119,\n",
       " 'beatcpi_4': 119,\n",
       " 'luckzpz_33': 119,\n",
       " 'luckzpz_35': 119,\n",
       " '紫葳侍郎_4': 119,\n",
       " '割总_2': 119,\n",
       " '林奇_5': 119,\n",
       " 'vittata_12': 119,\n",
       " '新人路过_0': 119,\n",
       " 'lzw1969_8': 119,\n",
       " '文明守望_17': 119,\n",
       " 'mysteed_6': 119,\n",
       " '波段分级_2': 119,\n",
       " 'JYDD2003_4': 119,\n",
       " '常存_5': 118,\n",
       " '评级上调_5': 118,\n",
       " '如_佛_1': 118,\n",
       " '伏尔泰_11': 118,\n",
       " 'SAYBYEA股_2': 118,\n",
       " '牛哥_2': 118,\n",
       " 'yugong1994_6': 118,\n",
       " '低姿态_17': 118,\n",
       " 'jk1968_4': 118,\n",
       " '温酒斩华佗_2': 118,\n",
       " '常存_7': 117,\n",
       " '学习再学习_2': 117,\n",
       " 'shuifeng2009_1': 117,\n",
       " 'chh919_0': 117,\n",
       " 'luckzpz_37': 117,\n",
       " 'hello123_1': 117,\n",
       " 'shuifeng2009_2': 117,\n",
       " '牛村村民_5': 117,\n",
       " 'zyc641124_1': 117,\n",
       " '学习再学习_3': 117,\n",
       " 'jsfq_2': 117,\n",
       " 'JYDD2003_8': 117,\n",
       " '与时间为友_11': 117,\n",
       " '拌拌_3': 116,\n",
       " '天书_15': 116,\n",
       " '低姿态_16': 116,\n",
       " 'xineric_6': 116,\n",
       " 'vittata_13': 116,\n",
       " '常存_6': 116,\n",
       " '毛之川_4': 116,\n",
       " 'ylshxajh_18': 116,\n",
       " 'xvz_0': 116,\n",
       " 'yh123556_1': 116,\n",
       " '评级上调_2': 116,\n",
       " '守心斋_9': 116,\n",
       " 'jiangdaya_9': 116,\n",
       " 'seeker24680_0': 115,\n",
       " 'blacklevi0823_8': 115,\n",
       " 'wxy466_0': 115,\n",
       " '天书_16': 115,\n",
       " 'shuijing_4': 115,\n",
       " '太原业余基民_2': 115,\n",
       " 'vittata_10': 115,\n",
       " 'freeman1980_5': 115,\n",
       " 'JYDD2003_7': 115,\n",
       " 'yanglee789_0': 115,\n",
       " '债券小白_2': 114,\n",
       " '酱油面_8': 114,\n",
       " 'silizhe168_3': 114,\n",
       " '量化投资先锋_15': 114,\n",
       " 'wss115_2': 114,\n",
       " 'coolie_6': 114,\n",
       " 'mysteed_5': 113,\n",
       " '穿风_6': 113,\n",
       " '没钱又丑_4': 113,\n",
       " '没钱又丑_6': 113,\n",
       " '临江之麋_7': 113,\n",
       " 'wss115_1': 113,\n",
       " '割总_3': 113,\n",
       " 'lzw1969_7': 113,\n",
       " 'xuyongnn_7': 113,\n",
       " 'freeman1980_8': 113,\n",
       " '守心斋_7': 113,\n",
       " 'memoriesaier_0': 113,\n",
       " '蔓越橘_18': 112,\n",
       " '东方龙2014_15': 112,\n",
       " 'cile_5': 112,\n",
       " 'tintin_2': 112,\n",
       " 'jsfq_4': 112,\n",
       " '胆小_8': 112,\n",
       " 'rotation_12': 112,\n",
       " 'viking_75_2': 112,\n",
       " '紫葳侍郎_3': 112,\n",
       " 'zyc641124_2': 112,\n",
       " '如_佛_3': 112,\n",
       " 'jk1968_6': 112,\n",
       " '花过水无痕_1': 112,\n",
       " 'chris1234_1': 112,\n",
       " '胆小_5': 112,\n",
       " '超弦资本_0': 112,\n",
       " '拌拌_2': 111,\n",
       " 'ZXFCD_2': 111,\n",
       " 'xuyongnn_5': 111,\n",
       " '临江之麋_6': 111,\n",
       " '抄顶逃底_8': 111,\n",
       " '牛村老叟_6': 111,\n",
       " '羽痕_7': 111,\n",
       " '牛死了_3': 111,\n",
       " '忧蓝_9': 111,\n",
       " 'freeman1980_7': 111,\n",
       " '牛明雷_3': 111,\n",
       " '李季峰_6': 110,\n",
       " 'a167387_2': 110,\n",
       " 'rypan_8': 110,\n",
       " 'xcgdgp_10': 110,\n",
       " '毛之川_3': 110,\n",
       " '帅牛_10': 110,\n",
       " '评级上调_4': 110,\n",
       " '牛村村民_4': 110,\n",
       " 'jk1968_8': 110,\n",
       " '量化投资先锋_16': 110,\n",
       " '温酒斩华佗_1': 110,\n",
       " '水无常形_6': 109,\n",
       " '蔓越橘_20': 109,\n",
       " 'yugong1994_7': 109,\n",
       " '我是刚来的_7': 109,\n",
       " 'zhaojinze778_2': 109,\n",
       " 'yiyayi_6': 109,\n",
       " 'shuijing_5': 109,\n",
       " '割总_4': 109,\n",
       " '花过水无痕_0': 109,\n",
       " 'starhill_7': 109,\n",
       " '海派柚子茶_1': 109,\n",
       " 'jack19921_4': 109,\n",
       " 'wlqzjr_3': 109,\n",
       " 'cjplove_4': 108,\n",
       " 'JYDD2003_9': 108,\n",
       " '张玉宁_1': 108,\n",
       " '东方龙2014_14': 108,\n",
       " '抄顶逃底_6': 108,\n",
       " 'xuyongnn_9': 108,\n",
       " '阿瞬_0': 108,\n",
       " 'luckzpz_40': 108,\n",
       " 'Baoman1971_5': 108,\n",
       " 'shine888_10': 108,\n",
       " '牛死了_4': 108,\n",
       " 'rotation_10': 108,\n",
       " 'king100_0': 108,\n",
       " '牛明雷_1': 108,\n",
       " 'gzyy_2': 108,\n",
       " 'silizhe168_2': 108,\n",
       " '周易转债_0': 108,\n",
       " '转债痴迷者_5': 107,\n",
       " 'locky_z_0': 107,\n",
       " '紫葳侍郎_5': 107,\n",
       " '梦想会计师_8': 107,\n",
       " 'xuyongnn_4': 107,\n",
       " 'jsfq_3': 107,\n",
       " 'silizhe168_7': 107,\n",
       " '伏尔泰_12': 107,\n",
       " '伏尔泰_15': 106,\n",
       " 'silizhe168_4': 106,\n",
       " 'wlqzjr_5': 106,\n",
       " '毛之川_6': 106,\n",
       " 'sUlla_4': 106,\n",
       " '西楼_9': 106,\n",
       " 'zyc641124_5': 106,\n",
       " 'lailai2000_8': 106,\n",
       " 'xuyongnn_8': 106,\n",
       " 'JYDD2003_5': 106,\n",
       " '子轩的Daddy_1': 105,\n",
       " '酱油面_9': 105,\n",
       " '鼎级外星人_0': 105,\n",
       " 'leichaoqun12_0': 105,\n",
       " '西胖子_12': 105,\n",
       " 'jk1968_7': 105,\n",
       " '牛明雷_0': 105,\n",
       " '胆小_4': 105,\n",
       " '阿瞬_1': 105,\n",
       " '胆小_9': 104,\n",
       " '李季峰_5': 104,\n",
       " 'blacklevi0823_9': 104,\n",
       " 'urfatu_4': 104,\n",
       " 'ylshxajh_19': 104,\n",
       " '小竹_5': 104,\n",
       " 'zhaojinze778_3': 104,\n",
       " 'shuifeng2009_3': 104,\n",
       " 'hello123_2': 104,\n",
       " '牛哥_1': 104,\n",
       " '牛村老叟_8': 104,\n",
       " 'lyp_xjtu_0': 104,\n",
       " 'ylshxajh_21': 104,\n",
       " 'zyc641124_0': 104,\n",
       " 'lwhgjy_6': 104,\n",
       " '没胆又穷_4': 104,\n",
       " 'ZXFCD_3': 104,\n",
       " '守心斋_8': 104,\n",
       " '守心斋_11': 104,\n",
       " '海派柚子茶_0': 104,\n",
       " 'annali_0': 104,\n",
       " '蔓越橘_17': 103,\n",
       " 'luckzpz_36': 103,\n",
       " '水无常形_4': 103,\n",
       " 'sUlla_5': 103,\n",
       " 'lingleishuimu_0': 103,\n",
       " 'vittata_11': 103,\n",
       " '水无常形_5': 103,\n",
       " '6019233124_3': 103,\n",
       " 'urfatu_2': 103,\n",
       " '花过水无痕_3': 103,\n",
       " 'gypeng123_1': 103,\n",
       " 'jk1968_5': 103,\n",
       " '石诺_3': 103,\n",
       " '守心斋_10': 103,\n",
       " '胆小_7': 103,\n",
       " '伏尔泰_13': 103,\n",
       " 'xvz_2': 102,\n",
       " '蔓越橘_19': 102,\n",
       " 'urfatu_3': 102,\n",
       " 'xwy203333_3': 102,\n",
       " '太原业余基民_4': 102,\n",
       " 'luckzpz_39': 102,\n",
       " '宿不移_0': 102,\n",
       " 'ylshxajh_20': 102,\n",
       " 'coolie_7': 102,\n",
       " 'spzxby_0': 102,\n",
       " 'shine888_9': 102,\n",
       " 'starhill_6': 102,\n",
       " 'xw0710_0': 102,\n",
       " '量化投资先锋_19': 102,\n",
       " '花园小琴_10': 102,\n",
       " '西胖子_13': 101,\n",
       " 'memphis330_0': 101,\n",
       " 'ZXFCD_1': 101,\n",
       " 'lzw1969_10': 101,\n",
       " '15260527807_1': 101,\n",
       " 'orchimike_3': 101,\n",
       " '远坂凛LOVE_3': 101,\n",
       " '帅牛_15': 100,\n",
       " '学习再学习_4': 100,\n",
       " '毛之川_5': 100,\n",
       " '转债痴迷者_7': 100,\n",
       " 'xuyongnn_6': 100,\n",
       " '天书_21': 100,\n",
       " 'zhaojinze778_4': 100,\n",
       " 'rypan_10': 100,\n",
       " '穿风_5': 100,\n",
       " 'ylshxajh_17': 100,\n",
       " 'mysteed_7': 100,\n",
       " '伏尔泰_14': 100,\n",
       " '文明守望_20': 99,\n",
       " '子轩的Daddy_2': 99,\n",
       " '小竹_6': 99,\n",
       " '天书_22': 99,\n",
       " '西胖子_11': 99,\n",
       " '牛村老叟_7': 99,\n",
       " 'cjplove_5': 99,\n",
       " '文明守望_19': 99,\n",
       " 'memphis330_1': 99,\n",
       " 'ZXFCD_4': 99,\n",
       " '我是刚来的_1': 99,\n",
       " '波段分级_4': 99,\n",
       " 'chenlei200332_0': 99,\n",
       " 'cile_7': 99,\n",
       " '花园小琴_11': 99,\n",
       " 'tintin_3': 98,\n",
       " '债券小白_3': 98,\n",
       " 'hello123_3': 98,\n",
       " 'cile_6': 98,\n",
       " 'yiyayi_8': 98,\n",
       " '新新新韭菜_0': 98,\n",
       " '天书_18': 98,\n",
       " '紫葳侍郎_6': 98,\n",
       " '梦想会计师_9': 98,\n",
       " 'king100_2': 98,\n",
       " '太原业余基民_3': 98,\n",
       " '不转不立_1': 98,\n",
       " '牛哥_3': 98,\n",
       " '张玉宁_3': 98,\n",
       " '牛村村民_6': 98,\n",
       " '低姿态_20': 98,\n",
       " '羽痕_6': 98,\n",
       " '忧蓝_7': 98,\n",
       " 'orchimike_2': 98,\n",
       " '花过水无痕_2': 98,\n",
       " 'gztom_0': 98,\n",
       " 'a167387_3': 98,\n",
       " 'lailai2000_9': 98,\n",
       " 'jk1968_9': 98,\n",
       " '韭歌_0': 98,\n",
       " '石诺_2': 98,\n",
       " 'jiangdaya_7': 98,\n",
       " 'matian001_1': 98,\n",
       " 'memoriesaier_1': 98,\n",
       " 'memoriesaier_2': 98,\n",
       " '健身教练去吸脂_5': 98,\n",
       " '阿瞬_3': 98,\n",
       " 'xyzhero_1': 97,\n",
       " '小竹_7': 97,\n",
       " '非凡猪_0': 97,\n",
       " 'yup77_0': 97,\n",
       " 'chh919_3': 97,\n",
       " 'wxy466_2': 97,\n",
       " 'rypan_9': 97,\n",
       " '转债痴迷者_6': 97,\n",
       " '长乐未央_1': 97,\n",
       " '割总_5': 97,\n",
       " 'Baoman1971_8': 97,\n",
       " 'coolie_9': 97,\n",
       " 'shine888_8': 97,\n",
       " 'chris1234_3': 97,\n",
       " 'suliang_2': 96,\n",
       " '常存_8': 96,\n",
       " '转债痴迷者_8': 96,\n",
       " '锋行_0': 96,\n",
       " '量化投资先锋_20': 96,\n",
       " '文明守望_18': 96,\n",
       " '鼎级外星人_1': 96,\n",
       " '穿风_7': 96,\n",
       " 'vittata_14': 96,\n",
       " '没胆又穷_5': 96,\n",
       " 'king100_1': 96,\n",
       " 'viking_75_3': 96,\n",
       " '守心斋_12': 96,\n",
       " 'matian001_0': 96,\n",
       " '海派柚子茶_2': 96,\n",
       " '子轩的Daddy_0': 95,\n",
       " '张玉宁_2': 95,\n",
       " 'yunzhou008_0': 95,\n",
       " 'yup77_1': 95,\n",
       " '水无常形_0': 95,\n",
       " '烙饼姐姐_0': 95,\n",
       " '6019233124_5': 95,\n",
       " 'aiplus_1': 95,\n",
       " '水与火_2': 95,\n",
       " '新人路过_2': 95,\n",
       " '封基越放越醇_0': 95,\n",
       " 'yh123556_4': 95,\n",
       " 'Baoman1971_6': 95,\n",
       " '周予樟_1': 95,\n",
       " '我是刚来的_4': 95,\n",
       " 'DPhil_G_11': 94,\n",
       " '量化投资先锋_18': 94,\n",
       " 'ZXFCD_6': 94,\n",
       " '帅牛_14': 94,\n",
       " '水无常形_10': 94,\n",
       " 'zzzzv_0': 94,\n",
       " 'yh123556_2': 94,\n",
       " 'lwhgjy_8': 94,\n",
       " '牛死了_6': 94,\n",
       " '牛死了_7': 94,\n",
       " 'chris1234_4': 94,\n",
       " '学习再学习_5': 94,\n",
       " 'gzyy_1': 94,\n",
       " '温酒斩华佗_3': 94,\n",
       " '债券小白_4': 94,\n",
       " 'yanglee789_1': 94,\n",
       " '没钱又丑_7': 93,\n",
       " 'cmx_1': 93,\n",
       " '如_佛_6': 93,\n",
       " 'vittata_17': 93,\n",
       " '天书_20': 93,\n",
       " '割总_6': 93,\n",
       " '牛哥_0': 93,\n",
       " 'shuifeng2009_4': 93,\n",
       " 'lyp_xjtu_2': 93,\n",
       " 'zzzzv_2': 93,\n",
       " '低姿态_22': 93,\n",
       " 'zhaojinze778_5': 93,\n",
       " '牛村村民_7': 93,\n",
       " 'xyzhero_3': 93,\n",
       " '没胆又穷_7': 93,\n",
       " '花过水无痕_5': 93,\n",
       " 'chris1234_2': 93,\n",
       " 'shysky123_1': 93,\n",
       " '波段分级_5': 93,\n",
       " 'JYDD2003_6': 93,\n",
       " 'neo牛3_23': 93,\n",
       " 'tintin_4': 92,\n",
       " 'jsfq_6': 92,\n",
       " 'blacklevi0823_10': 92,\n",
       " '低姿态_24': 92,\n",
       " '帅牛_12': 92,\n",
       " '帅牛_17': 92,\n",
       " 'xwy203333_4': 92,\n",
       " 'SAYBYEA股_3': 92,\n",
       " 'yiyayi_11': 92,\n",
       " 'SAYBYEA股_0': 92,\n",
       " 's52510447_1': 92,\n",
       " '西楼_8': 92,\n",
       " '宿不移_2': 92,\n",
       " '健身教练去吸脂_7': 92,\n",
       " '健身教练去吸脂_6': 92,\n",
       " 'orchimike_4': 92,\n",
       " 'viking_75_4': 92,\n",
       " '海派柚子茶_3': 92,\n",
       " '与时间为友_12': 92,\n",
       " 'freeman1980_9': 91,\n",
       " 'xuyongnn_10': 91,\n",
       " '我是刚来的_5': 91,\n",
       " '烙饼姐姐_1': 91,\n",
       " 'yechunfang_2': 91,\n",
       " '林奇_6': 91,\n",
       " 'wss115_3': 91,\n",
       " '低姿态_29': 91,\n",
       " '忧蓝_8': 91,\n",
       " '阿基米德_0': 91,\n",
       " 'matian001_2': 91,\n",
       " 'silizhe168_9': 91,\n",
       " '我是刚来的_3': 91,\n",
       " ...}"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_resp_dict.get('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1_234'"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x= '1_234_23'\n",
    "re.sub('_\\d+$','',x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc1=db['db_parker']['jsl_user_creator']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python3_64\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: insert is deprecated. Use insert_one or insert_many instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "ename": "InvalidDocument",
     "evalue": "documents must have only string keys, key was None",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvalidDocument\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-137-549997352d09>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdoc1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\python3_64\\lib\\site-packages\\pymongo\\collection.py\u001b[0m in \u001b[0;36minsert\u001b[1;34m(self, doc_or_docs, manipulate, check_keys, continue_on_error, **kwargs)\u001b[0m\n\u001b[0;32m   3159\u001b[0m             \u001b[0mwrite_concern\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mWriteConcern\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3160\u001b[0m         return self._insert(doc_or_docs, not continue_on_error,\n\u001b[1;32m-> 3161\u001b[1;33m                             check_keys, manipulate, write_concern)\n\u001b[0m\u001b[0;32m   3162\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3163\u001b[0m     def update(self, spec, document, upsert=False, manipulate=False,\n",
      "\u001b[1;32mc:\\python3_64\\lib\\site-packages\\pymongo\\collection.py\u001b[0m in \u001b[0;36m_insert\u001b[1;34m(self, docs, ordered, check_keys, manipulate, write_concern, op_id, bypass_doc_val, session)\u001b[0m\n\u001b[0;32m    605\u001b[0m             return self._insert_one(\n\u001b[0;32m    606\u001b[0m                 \u001b[0mdocs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mordered\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_keys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmanipulate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwrite_concern\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_id\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 607\u001b[1;33m                 bypass_doc_val, session)\n\u001b[0m\u001b[0;32m    608\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m         \u001b[0mids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python3_64\\lib\\site-packages\\pymongo\\collection.py\u001b[0m in \u001b[0;36m_insert_one\u001b[1;34m(self, doc, ordered, check_keys, manipulate, write_concern, op_id, bypass_doc_val, session)\u001b[0m\n\u001b[0;32m    593\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    594\u001b[0m         self.__database.client._retryable_write(\n\u001b[1;32m--> 595\u001b[1;33m             acknowledged, _insert_command, session)\n\u001b[0m\u001b[0;32m    596\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    597\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdoc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mRawBSONDocument\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python3_64\\lib\\site-packages\\pymongo\\mongo_client.py\u001b[0m in \u001b[0;36m_retryable_write\u001b[1;34m(self, retryable, func, session)\u001b[0m\n\u001b[0;32m   1241\u001b[0m         \u001b[1;34m\"\"\"Internal retryable write helper.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1242\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tmp_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1243\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_retry_with_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mretryable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1244\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1245\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reset_server\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maddress\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python3_64\\lib\\site-packages\\pymongo\\mongo_client.py\u001b[0m in \u001b[0;36m_retry_with_session\u001b[1;34m(self, retryable, func, session, bulk)\u001b[0m\n\u001b[0;32m   1194\u001b[0m                         \u001b[1;31m# Reset the transaction id and retry the operation.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1195\u001b[0m                         \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_retry_transaction_id\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1196\u001b[1;33m                     \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msock_info\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretryable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1197\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mServerSelectionTimeoutError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1198\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mis_retrying\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python3_64\\lib\\site-packages\\pymongo\\collection.py\u001b[0m in \u001b[0;36m_insert_command\u001b[1;34m(session, sock_info, retryable_write)\u001b[0m\n\u001b[0;32m    588\u001b[0m                 \u001b[0msession\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    589\u001b[0m                 \u001b[0mclient\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__database\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 590\u001b[1;33m                 retryable_write=retryable_write)\n\u001b[0m\u001b[0;32m    591\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    592\u001b[0m             \u001b[0m_check_write_command_response\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python3_64\\lib\\site-packages\\pymongo\\pool.py\u001b[0m in \u001b[0;36mcommand\u001b[1;34m(self, dbname, spec, slave_ok, read_preference, codec_options, check, allowable_errors, check_keys, read_concern, write_concern, parse_write_concern_error, collation, session, client, retryable_write, publish_events)\u001b[0m\n\u001b[0;32m    582\u001b[0m         \u001b[1;31m# Catch socket.error, KeyboardInterrupt, etc. and close ourselves.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    583\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 584\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raise_connection_failure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merror\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    585\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    586\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msend_message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_doc_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python3_64\\lib\\site-packages\\pymongo\\pool.py\u001b[0m in \u001b[0;36m_raise_connection_failure\u001b[1;34m(self, error)\u001b[0m\n\u001b[0;32m    743\u001b[0m             \u001b[0m_raise_connection_failure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maddress\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    744\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 745\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    746\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    747\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__eq__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python3_64\\lib\\site-packages\\pymongo\\pool.py\u001b[0m in \u001b[0;36mcommand\u001b[1;34m(self, dbname, spec, slave_ok, read_preference, codec_options, check, allowable_errors, check_keys, read_concern, write_concern, parse_write_concern_error, collation, session, client, retryable_write, publish_events)\u001b[0m\n\u001b[0;32m    577\u001b[0m                            \u001b[0mcompression_ctx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompression_context\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    578\u001b[0m                            \u001b[0muse_op_msg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mop_msg_enabled\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 579\u001b[1;33m                            unacknowledged=unacknowledged)\n\u001b[0m\u001b[0;32m    580\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mOperationFailure\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    581\u001b[0m             \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python3_64\\lib\\site-packages\\pymongo\\network.py\u001b[0m in \u001b[0;36mcommand\u001b[1;34m(sock, dbname, spec, slave_ok, is_mongos, read_preference, codec_options, session, client, check, allowable_errors, address, check_keys, listeners, max_bson_size, read_concern, parse_write_concern_error, collation, compression_ctx, use_op_msg, unacknowledged)\u001b[0m\n\u001b[0;32m    112\u001b[0m         request_id, msg, size, max_doc_size = message._op_msg(\n\u001b[0;32m    113\u001b[0m             \u001b[0mflags\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mspec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdbname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mread_preference\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslave_ok\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_keys\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 114\u001b[1;33m             codec_options, ctx=compression_ctx)\n\u001b[0m\u001b[0;32m    115\u001b[0m         \u001b[1;31m# If this is an unacknowledged write then make sure the encoded doc(s)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    116\u001b[0m         \u001b[1;31m# are small enough, otherwise rely on the server to return an error.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python3_64\\lib\\site-packages\\pymongo\\message.py\u001b[0m in \u001b[0;36m_op_msg\u001b[1;34m(flags, command, dbname, read_preference, slave_ok, check_keys, opts, ctx)\u001b[0m\n\u001b[0;32m    677\u001b[0m                 flags, command, identifier, docs, check_keys, opts, ctx)\n\u001b[0;32m    678\u001b[0m         return _op_msg_uncompressed(\n\u001b[1;32m--> 679\u001b[1;33m             flags, command, identifier, docs, check_keys, opts)\n\u001b[0m\u001b[0;32m    680\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    681\u001b[0m         \u001b[1;31m# Add the field back to the command.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInvalidDocument\u001b[0m: documents must have only string keys, key was None"
     ]
    }
   ],
   "source": [
    "doc1.insert(result_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " '硬核韭菜': 91,\n",
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       " '与时间为友': 84,\n",
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       " '玖富证券小犇仔': 80,\n",
       " 'pppppp': 80,\n",
       " '文明守望': 80,\n",
       " 'wuxin126': 80,\n",
       " 'zxd0424': 79,\n",
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       " 'jsxz8': 75,\n",
       " '一路向北': 75,\n",
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       " 'mingmingniu': 74,\n",
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       " 'ge75': 74,\n",
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       " 'chenlei200332': 74,\n",
       " '新人01': 73,\n",
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       " 'wuist': 73,\n",
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       " 'ETF量化与轮动': 73,\n",
       " 'lvshihong': 73,\n",
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       " 'Twenty': 63,\n",
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       " 'Yii': 58,\n",
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       " '吉地': 39,\n",
       " '淡定就好': 39,\n",
       " '银色奇迹': 39,\n",
       " 'atkatk': 39,\n",
       " '牛不疯': 39,\n",
       " '张又文': 39,\n",
       " 'zhaohx': 39,\n",
       " 'ppooqq': 39,\n",
       " 'knowledge666': 39,\n",
       " 'daxian100': 39,\n",
       " 'sbdqq': 39,\n",
       " '李季峰': 39,\n",
       " 'jetren': 39,\n",
       " '债股投资8': 39,\n",
       " 'wykkyle': 39,\n",
       " '严谨的国人': 39,\n",
       " 'twinssft': 38,\n",
       " 'xxldh': 38,\n",
       " 'z8256961': 38,\n",
       " '无情1': 38,\n",
       " 'gzyy': 38,\n",
       " '波段分级': 38,\n",
       " 'lzshf': 38,\n",
       " 'ruanyibing': 38,\n",
       " '大胆上': 38,\n",
       " '求索真相': 38,\n",
       " 'sm1314': 38,\n",
       " '非凡猪': 38,\n",
       " 'Lanmo': 37,\n",
       " '错过怀念': 37,\n",
       " '金8': 37,\n",
       " 'lkeys': 37,\n",
       " '颜小白': 37,\n",
       " 'tiaoyang': 37,\n",
       " '蜗牛也牛': 37,\n",
       " '冰川快车': 37,\n",
       " 'flyzizai': 37,\n",
       " '毛晓光': 37,\n",
       " 'dirlx': 37,\n",
       " '浩瀚红鹰': 37,\n",
       " '鼹鼠': 37,\n",
       " 'shshchen': 37,\n",
       " '夸克轻子粥': 36,\n",
       " 'xuyongnn': 36,\n",
       " 'ZXFCD': 36,\n",
       " 'smao': 36,\n",
       " 'Cuiyang': 36,\n",
       " 'stickying': 36,\n",
       " 'jingmushui': 36,\n",
       " 'cxm1127': 36,\n",
       " '韭韭韭韭韭菜': 36,\n",
       " 'magician': 36,\n",
       " '集思广益2016': 36,\n",
       " '每日1T': 36,\n",
       " '中本庸': 36,\n",
       " 'Edward_shangdi': 36,\n",
       " 'mtlxh': 35,\n",
       " '数亮投资01': 35,\n",
       " '鲁原': 35,\n",
       " '均金无忌': 35,\n",
       " '王员外NT': 35,\n",
       " '太平乡人': 35,\n",
       " '低风险策略': 35,\n",
       " 'aleck_jt': 35,\n",
       " 'huangzhen92': 35,\n",
       " 'acertdq': 35,\n",
       " '夜雨滂沱': 35,\n",
       " 'lijianri': 35,\n",
       " 'kykdu': 35,\n",
       " 'magic_gerrard': 35,\n",
       " 'npintel': 35,\n",
       " '贵州茅台600519': 35,\n",
       " 'viking_75': 35,\n",
       " '黄阳': 35,\n",
       " 'bitecoin': 35,\n",
       " 'zarathus': 35,\n",
       " '小蜜蜂磊磊': 35,\n",
       " '行者之道': 35,\n",
       " '自学者佛道': 35,\n",
       " '莱茵河之谜': 34,\n",
       " 'asdf33333': 34,\n",
       " 'eltonlau': 34,\n",
       " 'jeffrey810': 34,\n",
       " 'km_zxf': 34,\n",
       " 'ycyycy': 34,\n",
       " 'lwhgjy': 34,\n",
       " '亚历山大肉': 34,\n",
       " 'mmmdoor': 34,\n",
       " 'impblash': 34,\n",
       " '五月闲云': 34,\n",
       " 'Wangyuliang': 34,\n",
       " 'davidckk': 34,\n",
       " 'zbl181818': 34,\n",
       " 'weonline': 34,\n",
       " 'yonghuming888': 34,\n",
       " 'guoguo': 34,\n",
       " '海浪头头': 34,\n",
       " 'ifree2015': 34,\n",
       " 'spring214': 34,\n",
       " '海阔天空yu': 34,\n",
       " '分金街': 34,\n",
       " 'cile': 34,\n",
       " '盛唐风物': 34,\n",
       " '四国大战': 34,\n",
       " '简单明了出真知': 33,\n",
       " '好好大先生': 33,\n",
       " 'nuyoahz': 33,\n",
       " '没钱又老': 33,\n",
       " 'daphne2006': 33,\n",
       " '卖空调翁': 33,\n",
       " '食利者': 33,\n",
       " 'laneldarye': 33,\n",
       " 'cjplove': 33,\n",
       " '做正确的事': 33,\n",
       " '黄单车': 33,\n",
       " 'kickasha': 33,\n",
       " 'tominvest': 33,\n",
       " 'wpfxy1': 33,\n",
       " 'yym114': 33,\n",
       " 'Jankin': 33,\n",
       " 'jisilu': 32,\n",
       " 'tottijian': 32,\n",
       " 'video9790': 32,\n",
       " 'showhand': 32,\n",
       " '红糖饼': 32,\n",
       " 'cloud4661461': 32,\n",
       " '1062532177': 32,\n",
       " 'danding': 32,\n",
       " '巴菲特远房亲戚': 32,\n",
       " '玩个跷跷板': 32,\n",
       " '水流云在': 32,\n",
       " 'dy0678': 32,\n",
       " 'kaka1976': 32,\n",
       " 'realrich33': 32,\n",
       " 'caaa001': 32,\n",
       " '大垅2017': 32,\n",
       " '阿皮': 32,\n",
       " '58528997': 32,\n",
       " '13410685260': 32,\n",
       " 'jiucai1986': 32,\n",
       " '猎人投资笔记': 32,\n",
       " 'goodexp': 32,\n",
       " 'z383788052': 31,\n",
       " 'ericericeric': 31,\n",
       " 'hej009': 31,\n",
       " 'Jo': 31,\n",
       " 'byjs': 31,\n",
       " 'kubagou': 31,\n",
       " 'lushize': 31,\n",
       " 'newsu': 31,\n",
       " 'ltsomeone': 31,\n",
       " '搬砖工人': 31,\n",
       " 'espn7922': 31,\n",
       " 'ss3hj': 31,\n",
       " 'jzcqeling': 31,\n",
       " 'xsywinster': 31,\n",
       " 'mrwangting': 31,\n",
       " 'suliang': 31,\n",
       " '太保破发': 31,\n",
       " '有家才有国': 31,\n",
       " 'VV85': 31,\n",
       " '明心': 30,\n",
       " 'kino': 30,\n",
       " 'chineseumi': 30,\n",
       " '吴焱': 30,\n",
       " 'ztylsg1992': 30,\n",
       " 'gtheif': 30,\n",
       " 'lowrisker': 30,\n",
       " 'cooledit': 30,\n",
       " 'mahamud': 30,\n",
       " '子轩的Daddy': 30,\n",
       " 'wangxi': 30,\n",
       " 'old_xu': 30,\n",
       " '饮者': 30,\n",
       " '北冥神功': 30,\n",
       " 'gelu': 30,\n",
       " 'newone': 30,\n",
       " 'riyuec': 30,\n",
       " '键盘叔': 30,\n",
       " '太阳花': 30,\n",
       " '东逝水': 30,\n",
       " 'lgs11': 30,\n",
       " 'concept': 30,\n",
       " 'daniel1978': 30,\n",
       " 'big_fish': 30,\n",
       " '郁闷的老湿': 30,\n",
       " '加菲猫新': 30,\n",
       " '一切为了房价': 30,\n",
       " '铁锈带': 30,\n",
       " '感动自己': 29,\n",
       " '江南金马': 29,\n",
       " '人算不如天算': 29,\n",
       " 'changye': 29,\n",
       " 'richbb': 29,\n",
       " '魍者之语': 29,\n",
       " 'Keith_Ren': 29,\n",
       " 'acakedut': 29,\n",
       " 'grandriver': 29,\n",
       " 'uniquexl': 29,\n",
       " '青山老祖': 29,\n",
       " '日月': 29,\n",
       " 'laughter': 29,\n",
       " 'liteon': 29,\n",
       " '不出界': 29,\n",
       " 'sunnylu929': 29,\n",
       " 'ghalcyong': 29,\n",
       " 'kongl': 29,\n",
       " 'cnbjfl': 29,\n",
       " 'skdy': 29,\n",
       " '天道忌巧': 29,\n",
       " '风之彩': 29,\n",
       " 'fghyui': 29,\n",
       " 'romapass': 29,\n",
       " '哈哈尔': 29,\n",
       " '灰溜溜的贝塔': 29,\n",
       " '小白不懂了': 29,\n",
       " 'lxl2003848': 29,\n",
       " '六月的雪花花': 29,\n",
       " '星辰2018': 29,\n",
       " '烤韭菜最喜欢吃': 29,\n",
       " '学长的投资笔记': 29,\n",
       " 'lzw8077': 28,\n",
       " 'wukimedia': 28,\n",
       " 'sweetwang': 28,\n",
       " '不转不立': 28,\n",
       " '夜尽天明': 28,\n",
       " 'lvxiaozhou': 28,\n",
       " 'annie102802': 28,\n",
       " 'akaristar': 28,\n",
       " '爱在十字路口': 28,\n",
       " '路路顺丰': 28,\n",
       " 'Tailei': 28,\n",
       " 'weaveph': 28,\n",
       " '飘雪的百分之一': 28,\n",
       " 'jin888555': 28,\n",
       " '无所得': 28,\n",
       " '驽马': 28,\n",
       " 'xtxjj': 28,\n",
       " 'allenblue': 28,\n",
       " '往事': 28,\n",
       " 'wxhnhqx': 28,\n",
       " '大草坪': 28,\n",
       " '独狼静思': 28,\n",
       " 'msouzj': 28,\n",
       " '可要红': 28,\n",
       " 'xiao678biao': 28,\n",
       " 'feifeifei': 28,\n",
       " '爬虫': 28,\n",
       " '小屁': 28,\n",
       " 'A股上万点': 28,\n",
       " '鲸鱼船长': 28,\n",
       " '深四海': 27,\n",
       " '红牛牛1': 27,\n",
       " 'fkygy': 27,\n",
       " 'huyin01': 27,\n",
       " 'anmytmq': 27,\n",
       " 'freedom2': 27,\n",
       " 'jisilu1': 27,\n",
       " '重低音': 27,\n",
       " 'qa332211': 27,\n",
       " 'chang2068': 27,\n",
       " 'meizizi': 27,\n",
       " 'riset8848': 27,\n",
       " 'lyp_xjtu': 27,\n",
       " 'ricky51': 27,\n",
       " 'isgb': 27,\n",
       " '蚊子': 27,\n",
       " '知后行': 27,\n",
       " '永远的学生': 27,\n",
       " '爱吃三文鱼': 27,\n",
       " 'Chyjjsl': 27,\n",
       " 'zhao666000': 27,\n",
       " '天地玄黄宇宙洪': 27,\n",
       " 'playko': 27,\n",
       " 'topgear': 27,\n",
       " 'hahalicai': 27,\n",
       " '炳烁': 27,\n",
       " '明镜亦非台': 27,\n",
       " 'sunxiao8': 27,\n",
       " '太阳雨999': 27,\n",
       " '债券小白': 27,\n",
       " '银行分级B': 27,\n",
       " 'coolroysai': 27,\n",
       " 'caifu': 27,\n",
       " 'ftcl': 27,\n",
       " '鸟人': 27,\n",
       " 'sparkstar': 26,\n",
       " 'greener': 26,\n",
       " 'cww827': 26,\n",
       " '熊集思': 26,\n",
       " '上海渔卡': 26,\n",
       " 'znh': 26,\n",
       " 'pq_jisilu': 26,\n",
       " '夜雨天': 26,\n",
       " 'jash1745': 26,\n",
       " 'accot': 26,\n",
       " '独孤小强': 26,\n",
       " '老虎的金黄': 26,\n",
       " 'kinkouorder': 26,\n",
       " 'defeng101': 26,\n",
       " 'halliluia': 26,\n",
       " '梦想永驻我心': 26,\n",
       " '外白渡桥': 26,\n",
       " '丑丑': 26,\n",
       " '等待_等待牛市': 26,\n",
       " 'Carby': 26,\n",
       " 'xeclipse': 26,\n",
       " '一刀': 26,\n",
       " 'Lbcylgf': 26,\n",
       " 'datouqq111': 26,\n",
       " 'laizl': 26,\n",
       " 'ggjjff': 26,\n",
       " 'Luohanguo': 26,\n",
       " '望京SOHO19楼': 26,\n",
       " 'quid': 26,\n",
       " 'smaster': 26,\n",
       " '懒猫': 26,\n",
       " 'qgj8848': 26,\n",
       " '熊猫不回撤': 26,\n",
       " 'yyttcc705': 25,\n",
       " '鸡汤雪雪': 25,\n",
       " 'realbean': 25,\n",
       " '鲁迅': 25,\n",
       " 'ydmewjaiavyq': 25,\n",
       " '大金': 25,\n",
       " 'nhlknzy': 25,\n",
       " 'sun1977': 25,\n",
       " 'cooyee': 25,\n",
       " 'zerogas': 25,\n",
       " 'hjzj_1900': 25,\n",
       " 'goodoo': 25,\n",
       " 'pangyun': 25,\n",
       " 'm300126': 25,\n",
       " 'kxc0405': 25,\n",
       " 'kllt': 25,\n",
       " 'deathwait': 25,\n",
       " 'locky_z': 25,\n",
       " 'lmx88': 25,\n",
       " '开平小方': 25,\n",
       " '3A涡轮战斗鸡': 25,\n",
       " 'shanbixia': 25,\n",
       " 'dwgx': 25,\n",
       " 'wjx147258': 25,\n",
       " 'yizhucao': 25,\n",
       " '18927400612': 25,\n",
       " '飞驰的皮卡': 25,\n",
       " 'a_hermit': 25,\n",
       " '慕容雪辉': 25,\n",
       " '醉懂女人心': 25,\n",
       " '集思有成': 25,\n",
       " 'brokenboy': 25,\n",
       " '雨后彩虹608': 25,\n",
       " 'VitoZou': 25,\n",
       " '财经怪咖': 25,\n",
       " '弄潮投资': 25,\n",
       " 'vk369': 25,\n",
       " 'Ulduar': 25,\n",
       " '圆圆的土豆': 25,\n",
       " '长时直海': 25,\n",
       " 'hmhou_2002': 25,\n",
       " '一块炸猪排': 25,\n",
       " 'luckywang': 25,\n",
       " '琴声': 25,\n",
       " 'mikacat': 24,\n",
       " '马化腾': 24,\n",
       " '麦肯稀碎': 24,\n",
       " 'desper': 24,\n",
       " 'messi10': 24,\n",
       " '上善若氺': 24,\n",
       " '深宇': 24,\n",
       " 'hbbs123': 24,\n",
       " '混迹投资圈': 24,\n",
       " 'ly008': 24,\n",
       " 'earlzhang': 24,\n",
       " 'zhongzheng95': 24,\n",
       " 'sexxy': 24,\n",
       " '投资': 24,\n",
       " 'adamchen_2002': 24,\n",
       " '大熊': 24,\n",
       " 'xoxo444': 24,\n",
       " 'clar': 24,\n",
       " 'arseacy': 24,\n",
       " 'ziontyc': 24,\n",
       " 'shendq': 24,\n",
       " 'Q424545': 24,\n",
       " '八牛': 24,\n",
       " 'zzs12345': 24,\n",
       " '浩渺的星空': 24,\n",
       " 'song_song': 24,\n",
       " 'ToyJoy': 24,\n",
       " 'strongdogking': 24,\n",
       " '小阿奎': 24,\n",
       " '韭菜投资学': 24,\n",
       " '吴香槟': 24,\n",
       " 'x0z0y0': 24,\n",
       " 'xcgdgp': 24,\n",
       " 'wqsdm': 24,\n",
       " '90884525': 24,\n",
       " 'zhangweilin5': 24,\n",
       " 'zhkkai': 24,\n",
       " 'jinyouxiang': 24,\n",
       " 'woozico': 24,\n",
       " 'guoke123': 24,\n",
       " '狂龙十八浪': 24,\n",
       " 'frankxu': 24,\n",
       " '那些杜鹃花': 24,\n",
       " '尘同光和': 24,\n",
       " 'ccd001': 23,\n",
       " '满仓分级': 23,\n",
       " '克星巴克': 23,\n",
       " 'zjs': 23,\n",
       " '武晓丽证券': 23,\n",
       " 'looqi': 23,\n",
       " '333小熊猫': 23,\n",
       " '银行的铜钿': 23,\n",
       " 'sylar': 23,\n",
       " 'waynew': 23,\n",
       " 'jmchen75': 23,\n",
       " 'yoursdennis': 23,\n",
       " 'finalmouse': 23,\n",
       " 'haitun011': 23,\n",
       " 'jsyang': 23,\n",
       " '握沙的执着': 23,\n",
       " 'go6aor09': 23,\n",
       " 'helsinki': 23,\n",
       " 'Oasis': 23,\n",
       " 'tonywago': 23,\n",
       " 'alongside': 23,\n",
       " '无常无我': 23,\n",
       " '发烧友': 23,\n",
       " 'vansltz': 23,\n",
       " 'supervisor': 23,\n",
       " '风收益险': 23,\n",
       " 'elance': 23,\n",
       " 'lizhuoming2006': 23,\n",
       " '寿山': 23,\n",
       " 'nirvana_jsl2': 23,\n",
       " 'Dagrrrrr': 23,\n",
       " 'zhenglonggeng': 23,\n",
       " 'neo牛3': 23,\n",
       " '你太严肃': 23,\n",
       " 'zailushangwen': 23,\n",
       " 'zclplus': 23,\n",
       " '三坑人1': 23,\n",
       " '沉檀香云': 23,\n",
       " '草草说说': 23,\n",
       " ...}"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_list=[]\n",
    "for k,v in result_dict.items():\n",
    "    d={}\n",
    "    d['user_id']=k\n",
    "    d['post_count']=v\n",
    "    result_list.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pymongo.results.InsertManyResult at 0x6b032348>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc1.insert_many(result_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_resp_list=[]\n",
    "for k,v in result_resp_dict.items():\n",
    "    d={}\n",
    "    d['user_id']=k\n",
    "    d['post_count']=v\n",
    "    result_resp_list.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33593"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result_resp_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc2=db['db_parker']['jsl_user_resp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pymongo.results.InsertManyResult at 0x66bf0888>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc2.insert_many(result_resp_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计每天贴数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
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 },
 "nbformat": 4,
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